Advancements on the Adoption of SHM Damage Detection Technologies into Embraer Aircraft Maintenance Procedures
Structural Health Monitoring (SHM) has the potential to revolutionize aircraft maintenance. Less complex and less time-consuming procedures – when compared to current non-destructive inspection (NDI) technologies – allowed by SHM can reduce the amount of time and burden of the inspection tasks and, consequently, reduce costs. Reliable SHM systems will be able to automatically assess structural condition and inform maintenance personnel the presence of a structural flaw. In the long term, when fully automated and integrated, SHM will be able to inform the need for an intervention and to provide targeted information for planning maintenance actions and logistics. This will also allow the improvement of structural performance, and may eliminate certain scheduled inspection tasks. Over the years Embraer has investigated different SHM technologies, and two of them were selected for a more in-depth investigation. After demonstrating strong results on ground tests and in an Embraer- 190 flight test aircraft, Comparative Vacuum Monitoring (CVM) and Lamb Waves (LW) technologies were included in an effort to move SHM, in particular the concept of Scheduled Structural Health Monitoring (S-SHM), into routine use for aircraft maintenance procedures. Embraer teamed with the Airworthiness Assurance NDI Validation Center (AANC) at Sandia Labs and Azul Airlines in a joint effort with Agência Nacional de Aviação Civil (ANAC, the Brazilian civil aviation regulator) for the qualification of SHM technologies and to validate the performance of such systems in real-life operational environment. The work aimed to develop and carry out a qualification process for SHM damage detection systems, which includes laboratory tests for the assessment of detection capabilities in terms of Probability of Detection (POD) and tests with systems installed on a number of aircraft from Azul Airlines. Demonstration of equipment configuration, sensor interrogation procedures, systems’ resolutions, conditions during the execution of inspections, and other aspects, were addressed. A formal process for SHM implementation was reached with the close consultation of the regulatory agency, and the feasibility of the use of SHM systems was also demonstrated to support the validation.
FAA SHM Research Program and the Challenges in Civil Aviation
The FAA’s Structural Health Monitoring (SHM) research program began in 2011 and its primary focus was to determine the potential impact of introducing SHM on civil aircraft on FAA regulatory policy and guidance. The FAA funded investigators at the Sandia National Labs Airworthiness Assurance Nondestructive Inspection Validation Center (AANC) to assist the FAA in developing a research program based on the AANC’s past work experience in NDI validation for the FAA. The program included a survey of the aviation industry concerning SHM capability, a gap analysis of SHM research needs and to develop an SHM application that would clearly show the steps to possible approval. As part of the research project, a team consisting of Sandia National Labs AANC, the Boeing Corporation, Delta Air Lines, Structural Monitoring Systems, Anodyne Electronics Manufacturing Corporation were formed and selected the Comparative Vacuum Monitoring (CVM) system as the SHM test program on the B737 Center Wing Box Fitting. The team installed CVM sensors on several Delta Air Lines 737 aircraft, collected lab and flight test data sufficient to convince Boeing to approve this application and become the first successfully approved use of CVM in the civil aviation community. This presentation will discuss the challenges faced by the team and the FAA during its research program and as well as continuing hurdles still being faced for future SHM use on the civil aviation community.
SHM Challenges for Fixed-wing Military Aviation: Thoughts on Future AFRL R&D Projects
The US Air Force (USAF) and US Department of Defense have a long history of research and development in the exploration of on-board sensors being used for detection of damage in aircraft structures. Initial activities can be traced to the early 1980’s which led to an extensive on-aircraft assessment of acoustic emission based (i.e. “passiveâ€) sensor system. In the late 1990’s an effort was launched to revitalize the capability which cumulated in the “Hot Spotsâ€ program which explored the use of an ultrasonic guided wave (i.e. “activeâ€) sensor system. Each of these programs encountered challenges that have hindered the use of these technologies on fixed-wing military aircraft. This paper briefly reviews these previous efforts, present current USAF Military Standards that define Structural Health Monitoring (SHM) for fixed wing aircraft, and provide a discussion of current and future concepts for research and development to resolve these challenges and enable eventual adaptation of SHM for fixed-wing applications. This will include a summary of current initiatives within the Materials and Manufacturing Directorate of AFRL and notional thoughts on potential projects for future developments required for this capability to be applied to fixed wing military aircraft.
Ultrasonics, Corrosion and SHM - the Story of Permasense Ltd.
The oil and gas sector produces fuels that meet more than 50% of the world’s energy demand. It handles unrefined fluids that come straight from a reservoir deep in the earth’s crust. The crude oil often is an unpredictable cocktail that constantly corrodes and erodes the plants that have been built to refine it. While corrosion costs the world about 3% of GDP/annum, the oil and gas sector proportionally spends much more to guarantee integrity of its assets. Unexpected shut downs cost millions of dollars per day in lost production alone, however catastrophic failures can be even more costly. Historically, to mitigate the risk of failures, refineries carry out wall thickness surveys at regular intervals by performing manual spot check ultrasonic thickness measurements. These infrequent measurements had poor repeatability. Our research at Imperial College London resulted in the development of the first wireless, permanently installable ultrasonic thickness gauge, delivering precise on demand measurements. The spin-off company Permasense Ltd was formed to develop and commercialise the technology with the aim to make a complete SHM system available to industry. To date the company has supplied more than 15000 sensors to the O&G industry and more than 15 million wall thickness measurements have been made. In 2016 Permasense Ltd was acquired by Emerson, a multi-national corporation. This talk will describe how the system works and challenges we encountered when bringing the technology from the lab to the field. Finally, we will reflect on general lessons that can be learned from the process.
A Damage Imaging Method Based on MUSIC Algorithm of Linear Sensor Array for Aircraft Structure
The performance and behavior characteristics of in-service aircraft structures can be affected by damage from external conditions such as impact, loading abrasion and corrosion, which seriously influences the safety of aircrafts. Hence, damage monitoring and localization has become a topic of significant interest in structural health monitoring (SHM) and widely researched. Since the guided wave is sensitive to small damages and can monitor large area region, guided wave based SHM method is one of the most potential SHM methods for aircrafts. Recently, multiple signal classification (MUSIC) is applied to the guided waved based SHM method for damage localization. In this paper, a damage imaging method, which combines the MUSIC (Multiple signal classification) algorithm and linear sensor array, was proposed and used for monitoring the aircraft structure damage. This method utilized the array signal processing method based on MUSIC algorithm, which performed the eigenvalue decomposition of the covariance matrix of array signal and then calculated the spatial spectrum by direction scanning to realize damage imaging. The method had an advantage of arranging the linear sensor array easily. The experiment on the aircraft structure had been established. Experimental results showed that the proposed method can realize damage imaging on aircraft structures accurately and the damage localization error was less than 2 cm.
A Robust and Effective Approach Towards Accelerated Dent-mapping using a Purposebuilt 3D Optical Inspection Tool
Dent-mapping of the aircraft skin remains a critical component in today’s maintenance, repair, and overhaul (MRO) industry. In addition to effectively and efficiently mapping dents on aircraft during regularly scheduled Base Maintenance, inspectors on the ground are under much higher time-pressure to complete the same dent-mapping task with high accuracy during unplanned Line Maintenance. This is a phenomenon that constantly plagues the MRO industry as pressure to reduce aircraft turn-around-time (TaT) continues to increase. Traditional manual methods are subjective, inaccurate, inconsistent, and extremely time-consuming. In pursuit of a technology that offers total efficiency gains in an end-to-end digital dent-mapping workflow, 8tree proposed the following requirements: (1) Zero-programming and minimal learning curves so that workers of all skill levels can operate the tool; (2) No special surface preparation requirements; (3) No post-processing software packages or need to develop specialized metrology knowledge; (4) Handheld portability and ease of maneuvering. This paper highlights the successful integration of a 3D digitization technique – structured-light scanning, and the proprietary analytics developed by 8tree to solve chronic surface inspection problems within the MRO industry. The principle of operation of dentCHECK®, a novel 3D digital surface inspection tool, as well as the design principles that helped shape the product during the early product development days will be discussed. In addition, two use cases – blend out assessment, and lightning strike assessment will also be discussed.
Characterization of Lamb Waves Propagation Behavior in Friction Stir Welded Joints of Dissimilar Materials
Friction Stir Welding (FSW) is a modern solid-state welding technique invented in 1991 at the Welding Institute of Cambridge. It is becoming popular and efficiently used in many applications including automotive, marine, railway and aircraft industries. FSW is vulnerable to the existence of various kinds of defects that may arise from the inappropriate selection of weld parameters. Buried defects such as voids and tunnels necessitate the existence of appropriate non-destructive evaluation (NDE) techniques. Guided waves are ultrasonic elastic waves that have the ability to travel over long distances. Their ability to propagate in complex structures and in both metallic and nonmetallic materials, has drawn the attention of many researchers to explore the potential of their use in detecting surface and internal flaws. However, defect-inspection in dissimilar friction stir welded joints is associated with several challenges related to the difference in the attenuation behavior between the two mediums. The purpose of this work is to scrutinize the interaction behavior of Lamb waves with friction stir welded joints of dissimilar materials. An AZ31B/AA6061-T6 FSW plate was used for investigation. The frequency-wavenumber filtering technique of the data extracted from a laser Doppler vibrometer (LDV) test, was used to filter out the reflected wave from the incident and transmitted waves. Reflection was observed from the weld zone of the used plate. A sensor network was designed to capture the drop of the incident signal, straight before and straight after the weld zone. This drop was used to measure the reflection generated upon the interaction of guided waves with the weld. The results showed that the average reflection, of the incident wave, generated when the wave crosses from AZ31B to AA6061-T6 base metal was around 10% higher in comparison to when the wave crosses from AA6061-T6 to AZ31B.
Delamination Detection of Composites Based on a Bivariate Gamma Function Approach
This study performs an inverse detection of stiffness degradation occurred by the delamination in laminated composite plates under impact loading. Five unknown parameters are considered to determine the delamination region, which is a modified form of the bivariate Gamma distribution function. The validity of the technique is numerically verified using a set of acceleration data obtained from a finite element simulation of laminated composites. The numerical examples show that the proposed technique is a feasible and practical method which can prove the location of a delamination region as well as inspect the distribution of deteriorated stiffness of composites.
Detecting Cracks in Helicopter Frame Station Structure using Unified Change Detection
A unified change detection approach was proposed recently for detecting and identifying changes caused by various types of faults in rotating machinery. This novel, but unsophisticated method was validated using vibration data from rotating machines with several different types of faults, e.g. gear, bearing, pump, shaft and planet carrier faults. This paper further explores the application of unified change detection to the structural health monitoring field. We will present results of applying the method to detecting a crack in a Black Hawk helicopter frame structure using the in-flight strain gauge data, which were collected during a flight trial. During the trial, an excessive level of stress was detected in the 53rd test flight hour at a strain gauge located on a forward frame structure and by follow-up inspection a structural crack was found in close proximity to the gauge location. A previous study showed that, with the development of the structural crack, the dominant frequency components around the 4th shaft order of the main rotor were shifted from their nominal position by approximately 10 percent of the main rotor shaft order and were observed to vary with time (i.e. a crack-induced nonlinear effect). For this paper, the strain data acquired at various locations around the crack location have been analyzed using the unified change detection approach. Analysis results show that the changes in structural loading due to the crack can be detected and trends indicating a gradual change in behaviour are observed, even under the most common and uniform maneuvers such as the in-ground effect (IGE) hover that was experienced in every flight of the trial. The crack-induced change can be detected by most strain gauges around the crack location.
Development of a Remote Acquisition and Transmission System of Strain Measurements in an Unmanned Aerial Vehicle for Damage Detection
Reliability is a key requirement in the aerospace industry. Therefore, structural health monitoring (SHM) applications using strain-field estimation and pattern recognition techniques are in development as an alternative to improve reliability and reduce maintenance costs, promising to ease damage detection on aerospace structures. However, some challenges need to be resolved before real implementation of these techniques. One of these challenges is to uncouple operational conditions changes in strain field from those related directly to damage. In this paper, the development of a strain measurement remote acquisition and transmission system on an unmanned aerial vehicle (UAV) using Fiber Grating Sensors (FBGs) is presented. Before flight testing, ground tests are carried out to emulate the dynamic loads that will be presented during flight. The strain data acquired are processed using unsupervised learning algorithms based on Self-Organizing Maps (SOM) and DS2L-SOM (density-based techniques) in order to classify different operational conditions. The results showed the capability of the system for classifying different load conditions for a UAV’s main beam.
Evaluation of NDT by Robotic Line Scan Thermography on Composite Aircraft Parts
Non-destructive testing ensures the structural integrity and reliability of composite structures produced in the aerospace industry. Conventionally adopted inspection techniques, such as water-coupled ultrasonic installations using a water jet, are not optimized for the complex geometries of newly emerging composite parts and are characterized by long inspection times and expensive equipment investments. A promising alternative is line scan thermography (LST), which can serve as a relatively quick, low-cost and efficient NDT method for curved monolithic and sandwich CFRP panels with a limited thickness. This paper describes the evaluation of an industrial prototype developed to perform LST inspections on a couple of reference aircraft parts embedded with artificial defects. Initial tests have shown that the complete LST inspection of surface areas up to 1.4 m2 takes around 15 min. In this way, the inspection time of a bottom panel and monolithic root fairing could be cut back by 80% and 70% respectively compared to conventional ultrasonic inspection methods. Inspection depths up to 6 CFRP plies, approximately 1.5 mm, were achieved for artificial defects as small as 4 mm in diameter (except for the inclusion defects ‘separator film’and ‘polyethylene’). Results are expected to improve even further once the inspection hardware has been made more manoeuvrable and the inspection parameters have been tuned to their optimal values.
Guided Elastic Waves in CFRP Plates with Random Material Properties
Structural health monitoring (SHM) of modern composite materials, e.g. carbon fiber reinforced plastics (CFRP), is a challenging task. Their complex microstructure has a strong influence on the propagation of ultrasonic guided waves. The classical deterministic finite element (FEM) does not take this fact into account. In the current work, an approach for the simulation of waves in structures with random material properties is presented. An important element in the analysis of uncertain systems is the representation of the stochastic properties in such a manner that they can be implemented in a finite element formulation of the given problem. This is then called stochastic finite elmenent method (SFEM) and can be seen as an extension of the classical FEM. The application of the SFEM to two-dimensional (2D) elastic wave propagation problem is presented. The Youngs modulus is modeled as a 2D random field in two ways. In the first case it is constructed with the Karhunen-Loève Expansion (KLE) and in the second case, with the integration point method.
Health Monitoring of Full-Scale Aerospace Systems using Networked Acoustic Emission Sensors
Acousto-ultrasonic health monitoring of composite materials and geometrically complex systems, such as a full-scale air vehicle, is a very complex process. In full-scale systems, the acoustic wave travels through several interconnected and acoustically mismatched pieces such as sandwich core panels, stringer stiffened skins, lugs, and holes at various velocities while undoing multiple reflections, refractions, and mode changes before reaching a surface mounted transducer. Existing techniques for acoustic emission (AE) source location and severity analysis rely on the assumption of constant wave speed and uninterrupted propagation path between sources and sensors. In complex fullscale systems this assumption, however is not valid. The goal of this paper is to present a distributed data centric acoustic emission sensor network and data processing algorithm for in-situ SHM of a full-scale air vehicle. The AE sensor network uses several lightweight direct-wave and network-fence sensors as well as various acquisition control time parameters to filter noise and minimize effects of cascading, reflection, and signal distortion. Source location is performed using a time-offset method with the assumption of material quasi-isotropy. Results from case study involving a full-scale composite fuselage instrumented with multiple AE sensors have shown that AE based SHM in geometrically complex and full-scale systems, despite exceedingly challenging, can be performed with reasonable accuracy using networked sensors and a series of adaptive signal acquisition control parameters.
Operational Load Monitoring System Implementation for Su-22UM3K Aging Aircraft
In many cases, aircrafts operate long beyond originally identified retirement times while they can effectively perform their mission and are economically viable to operate. However, keeping an aircraft airworthy includes the engineering analyses and tests needed to determine an adequate maintenance plan to retain operational safety and reliability. Su-22 is a variable-sweep wing fighter – bomber, which has been used in Polish Air Force for over three decades. In 2014 the decision was made, to retain a part of the aircraft population in service and increase their lifespan for next ten years, for both single- (Su-22M4K) and twin-seated version (Su-22UM3K). Twin seated Su- 22UM3K is used for training new pilots as well as sustaining good flight habits in more experience crew. As a consequence, that aircraft version is more threatened with higher loading spectrum both during flight and landing. To ensure a high operational safety of that aging aircraft, Life Extension Program was performed. Flight tests with real-time sensor system to determine strain and load spectrum during typical manoeuvers as well as full scale fatigue test of the airframe to ensure reliability and define critical inspection areas were carried out. Finally, Operational Load Monitoring (OLM) System was designed and permanently implemented on-board for real time strain measurement of some critical structure elements. In the paper, an approach to real time strain monitoring of critical structural components and aircraft condition assessment using on-board data acquisition unit is presented. The OLM System concept and architecture as well as measurement examples and system maintenance procedures are briefly described.
Optical Fiber Based Structural Integrity Monitoring of UAV Structure during Full-Scale Static and Fatigue Tests
An optical fiber based Rayleigh backscattering distributed strain sensing system was adopted as the main structural integrity monitoring tool for the IAI's HERON-TP UAV Full-Scale fatigue and ultimate tests. The strain signature along all major structural elements, as measured by the optical fibers, at each loading step was recorded and analyzed in real time. A specially developed human interface enabled easy tracking of emerging damage-related non-linear phenomena. This sensing concept reduces the need for adding hundreds of electrical strain gauges and eliminates intermediate conventional structural inspections during test, all leading to reducing test duration and cost.
Optical Fiber Sensor Based Aircraft Structural Health Monitoring System
The authors have developed an optical fiber based impact damage detection system for composite airframe structure. The objective is to predict the best maintenance timing to minimize unexpected delays and lost opportunities of aircraft operations. The implementation of this technology is being studied jointly with Airbus through the JASTAC II program. A full-scale structure validation test was performed jointly and demonstrated the capabilities of this technology. Other applications tested and demonstrated by the authors include the blunt object impact (BOI) detection and life cycle monitoring.
Structural Health Monitoring of a Military Aircraft using an Analog and Fiber Optic Sensor-based Data Acquisition System for Structural Life Management
In the design and development phase, the structural design life of aircrafts is assessed by structural analyses and tests with the baseline design load spectra. The structural life should be reassessed and updated periodically with actual and operational load spectra in the structural life management phase. According to ASIP (Aircraft Structural Integrity Program), an initial FSMP (Force Structural Maintenance Plan) containing inspection, repair and maintenance plans should also be updated with results of reassessed structural life for cost-effective maintenance and operational sustainability. The actual load spectrum is generated by using structural responses like strains, load factors and various flight parameters. In order to collect these kinds of data for military aircrafts, a data collection and analysis system which is named SIDAS(Structural Integrity Data Acquisition System) is currently under development as an SHM(Structural Health Monitoring) system. The architecture of SIDAS is similar to conventional usage monitoring systems, and it is composed of an onboard device and a ground analysis device. The onboard device includes two analog sensor modules, an fiber optic sensor module, a power supply module and a recorder modules. Strain gages, accelerometers and FBG (Fiber Bragg Grating) sensors are attached on major structures and they measure structural responses. Additionally, various flight parameters that are related to structural behavior are recorded through MIL-STD-1553B communication protocol in the onboard device. The ground analysis device consists of a set of hardware and software, and downloads collected data in the onboard device using Ethernet or a memory card to analyze those data and construct database. This paper contains basic design concepts of SIDAS and analysis results of its collected data from fatigue and taxi ing tests of a military aircraft in a design and development phase. The analysis results will ultimately be used for the structural life management.
Structural Health Monitoring of Turbomachinery using TOA Signal and Expert Software
The article portrays a holistic approach to fatigue prevention of turbomachines, based on: 1) rotating state observer of e.g. compressor or turbine blades; 2) tip timing method of indirect, contactless vibration measurement of rotating elements; 3) theoretical basics of sensor working; 4) expert analysis of measurement signal taking into consideration the modal characteristics of rotating state observer, spectra of monitored dynamic phenomena and characteristics of measurement chain; 5) a proactive intervention of the user in fatigue processes – by optimization of operation process of a turbomachine on the basis of the results of diagnostic tests, before damage occurs. The discussed subject matter was illustrated with examples of aviation engines, steam turbines, as well as industrial fans. Upon above 20 years of operational experience in the aviation of the Armed Forces of the Republic of Poland were proved both the purposefulness of a complex analysis of time of arrival (TOA) measurement signal and user intervention in fatigue processes arising out of human mistakes occurring during production, repair, operations, staff training and aviation supervision and user intervention on the stage preceding a fatigue cracking. The obtained results confirmed the purposefulness of using data from structural health monitoring (SHM) to interfere in fatigue processes taking place in antropotechnical device under testing (DUT, in the machine and its environment).
The Potential for Health Monitoring in Expandable Space Modules: The Bigelow Expandable Activity Module on the ISS
Expandable modules for use in space and on the Moon or Mars offer a great opportunity for volume and mass savings in future space exploration missions. This type of module can be compressed into a relatively small shape on the ground, allowing them to fit into space vehicles with a smaller cargo/fairing size than a traditional solid, metallic structure based module would allow. In April 2016, the Bigelow Expandable Activity Module (BEAM) was berthed to the International Space Station (ISS). BEAM is the first human-rated expandable habitat/module to be deployed and crewed in space. BEAM is a NASA managed ISS payload project in partnership with Bigelow Aerospace. BEAM is intended to stay attached to ISS for an operational period of 2 years to help advance the technology readiness for future expandable modules. BEAM has been instrumented with a suite of space flight certified sensors systems which will help characterize the module’s performance for thermal, radiation shielding and impact monitoring against potential Micro Meteoroid/Orbital Debris (MM/OD) providing fundamental information on the BEAM environment for potential health monitoring requirements and capabilities. This paper will provide an overview of how the sensors/instrumentation systems were developed, tested, installed and an overview of the current sensor system operations. It will also discuss how the MM/OD impact detection system referred to as the Distributed Impact Detection System (DIDS) data is being processed and reviewed on the ground by the principle investigators.
A Big Data Management and Analytics Framework for Bridge Monitoring
Bridge health monitoring involves massive volume of data with diverse and complex data types. While the data can potentially enhance the diagnostic and prognostic analyses of the structural condition of a bridge, the massive volume and the complexity of the data pose fundamental management and processing issues. Current practice of bridge health monitoring relies on proprietary servers and legacy data management tools, which are not well suited to meet today’s big data processing and management requirements. This paper discusses a cyberinfrastructure framework that takes advantages of state-of-the-art computing technologies to handle the data issues in bridge monitoring applications. We explore cloud computing as a scalable and reliable computing infrastructure service offered by cloud vendors. The use of a distributed NoSQL database system with cloud computing infrastructure facilitates scalability of data storage. In addition, the distributed computing resources in the cloud environment can be dynamically scaled on demand. The proposed framework is implemented for the monitoring of bridges located along the I-275 corridor in Michigan. The framework can effectively cope with changing demands for data management and processing in bridge monitoring.
A Multiscale Multispectral Approach to Digital Image Correlation for SHM Applications
A novel technique for digital image correlation (DIC) targeting structural health monitoring (SHM) applications is presented. The method uses multispectral imaging enabling simultaneous data acquisition at variable fields of view (FOV). In general, for a given FOV, appropriate speckle sizes have to be a priori defined, limiting the capability of acquiring viable DIC data from variable working distances or with variable accuracy. Furthermore, straightforward application of multiscale DIC patterns that can be seen in the same wavelength of light can cause measurement errors, forcing the use of larger subset sizes and thus reducing the spatial resolution of the deformation measurements. To overcome such limitations, a patterning technique accounting for camera resolution, distance to target, metric-based optimization and variable wavelength is developed. Specifically, patterns are created for two length scales and applied onto a structure. A black on white speckle pattern for far field measurements in addition to an ultraviolet pattern for near field measurements. The patterns were shown to optimally perform at the a priori calculated working distances. The potential of this method for use in SHM applications is discussed.
An On-site Evaluation Method for Corrosion Degradation of In-service Cable Wires by Image Histogram Matching
This paper proposes a novel non-destructive method by image histogram matching to evaluate the corrosion degradation of cable wires in service quantitatively. Artificial accelerated salt spray test is adopted to simulate the corrosion environment, and adequate RGB images are taken by a consumer-grade camera to cover the whole information of corroded surface for each specimen under different corrosion periods. Histogram distribution analysis is applied to study the concentrated modes of RGB images, and joint Gaussian distribution is fitted to capture location parameters. Timevariant models of the location parameters are regressed and then the mechanical properties are learnt by tensile and fatigue tests. Once the features of RGB corrosion images of steel wires are matched to the proposed time-variant models, the corresponding corrosion degree and mechanical properties can be estimated. The proposed method is meaningful for the on-site evaluation of cable wires in service with no need to carry on corrosion and mechanical degradation tests after replacing cables.
Crack Detection of Orthotropic Plate Box Girder Bridges Based on FBG Strain Sensors
Serving as one of the most common measurements in the instrumented Structural Health Monitoring system of bridges, strain response measurement, especially longterm measurement on a continuous basis can provide ongoing information of bridge structures on its structural behavior and health condition under external load. However, the study on strain response is far from adequate. Based on the Fiber Bragg Grating (FBG) strain sensors, this paper conduct a study on the strain response feature of steel box girder on the important elements and critical locations, and develop a statistical paradigm for crack detection. The local response property of strain response under vehicle load is highlighted, since it enables the measurement data to tell the vehicle load events that occur to the area nearby the sensors and to tell the health condition of this area. Features of strain response subject to vehicle load is first studied, its timefrequency domain feature is used to detect vehicle load events and break the strain measurement series into segments that corresponded to the detected vehicle load events. Based on the matching technique of segments of strain series from successive sections along the traffic direction, strain response segments of different sections under the identical vehicle load is obtained. Extracting the amplitude of their changes in the value subject to the identical vehicle load and evaluating their mutual ratio (the indicator) to mitigate eliminate different affects due to vehicle weight for each passing heavy vehicle (sample), the paradigm finally collects a dataset of the condition of different sections, by analysing the statistical property of this indicator, it detects the crack on the nearby region of the sensor location and infer the condition of this region.
Damage Evaluation on Steel Plate Reinforced RC Slabs with One-side Access AE Tomography via Anchor Bolts
Severe deterioration in aging infrastructures, especially in concrete structures, is currently becoming a critical issue world-wide. Since the preventive and proactive maintenance of infrastructures are necessary for repair before approaching to the final phase as the appearance of large-scale deterioration, efficient non-destructive testing (NDT) is imperative for the damage evaluation of infrastructures. In this concern, we have developed acoustic emission (AE) tomography technique as the promising NDT. AE tomography can estimate distributions of elastic-wave velocities and attenuationrates nondestructively inside reinforced concrete (RC) members. These distributions are to be readily associated with the degradation. In this study, AE sensors and accelerometers were employed to measure AE activities in steel-plate RC slabs. In order to examine monitoring areas in comparison with those of AE sensors, AE activities were detected by the accelerometers attached to heads of anchor-bolts, which were applied as temporary fixing tools to install reinforcing steel-plates at the bottom of RC slab. In the case of deteriorated structures, the secondary AE activity is generated from existing defects induced by traffic loads. Consequently, AE tomography analysis was carried out from the arrival times and the amplitudes of the secondary AE activities. It is found that damaged areas evaluated by one-side access AE tomography from the distributions of elastic-wave velocities and signal attenuations are in remarkable agreement with locations of internal cracks observed from on-site core-drilled samples.
Developing Integrated Methods and Software Tools for Monitoring-based Asset Performance Management
With the increasing concerns on aging infrastructure and environmental sustainability, structural health monitoring is not only the integral part of intelligent infrastructure, but also plays an essential role for infrastructure operators and owners to achieve cost-effective and carbon-efficient design, construction and operation. The ubiquitous applications of sensing and scanning technologies enable engineers to conduct monitoring-based asset performance assessment and decision-making. It raises the needs for developing the integrated methods and software tools for facilitating structural health monitoring and the data analysis. This paper presents our latest research in sensor placement optimization, the data analytics for extracting useful information from conventional sensors, e.g. accelerometers and vison sensors, e.g. video cameras, conducting efficient data-driven model by deep learning, undertaking model calibration via parallel optimization and eventually empowering engineers for model-based asset management. The research leads to the development of the integrated software tools for various infrastructure industry applications, including but not limited to urban water systems, buildings and bridges. The use cases illustrate the benefits and new challenges of the expedited analysis, robust data-driven model, and monitoring-based performance modeling for the informed asset management decisions
Development of SMART Gate Decision Support Tools for Large Inland Navigation USACE Infrastructure: The Dalles Tainter Gate
The US Army Corps of Engineers (USACE) recently replaced the upstream Tainter gate controlling the navigation channel of The Dalles Lock and Dam, just east of Portland, Oregon, and was interested in the development of decision support tools to support operation and maintenance of the gate under the Structural Monitoring and Analysis in Real-Time of lock Gates (SMART Gate) program. This paper focuses on the structural health monitoring (SHM) strategy developed by the Engineer Research and Development Center (ERDC) for long-term monitoring of the new upstream gate. A numerical model was developed to perform simulations of the new gate, evaluate various damage scenarios, and determine features that could indicate the simulated damage. The damage indication features were then used to develop an appropriate instrumentation package. The instrumentation of the gate included strain gages, temperature gages, and inclinometers. The instrumentation strategy was developed to provide real time operational feedback related to hoisting forces and uneven hoisting of the gate, biaxial bending moments, shear, and axial forces in the struts, and trunnion friction. At the time that the paper is being written, data is currently being collected from the instrumented gate and will be used to validate the statistical and numerical models. The instrumented gate and validated numerical models will then be used to test damage-detection tools and strategies. Once validated, the damage-detection algorithms will be applied to the data acquisition system on site, to inform maintenance and provide real-time alerts for operations.
Dynamics Testing for Alkali-Silica Reaction Diagnosis in Concrete.
This research is investigating a probabilistic framework for health diagnosis and prognosis of aging concrete structures in nuclear power plants. The current work focuses on degradation caused by ASR (alkali-silica reaction), which is a chemical reaction between the cement and certain aggregates containing amorphous silica – in the presence of water the ASR gel will expand causing damage to the concrete structure. Several different monitoring techniques — infrared thermography, digital image correlation (DIC), mechanical deformation measurements, nonlinear impact resonance acoustic spectroscopy (NIRAS), and vibro-acoustic modulation (VAM) —are being studied for ASR diagnosis of the specimen. This paper reports on the VAM study, which uses two simultaneous excitation frequencies; the interaction of these frequencies indicates damage through the presence of sidebands. Multiple resonant frequencies are considered along with their corresponding mode shapes. The possibility for damage localization is investigated.
Experimental Study on the In-situ Stress Measurement of Reinforced Concrete Structure by the Ring-Hole Method with FBG Sensor
Reinforced concrete structure is widely used in civil infrastructure engineering. In order to ensure the service safe of those infrastructure, periodic inspection and evaluation is necessary. Stress is an important indicator of existing structural evaluation. How to determine the in-situ stress of reinforced concrete structures is basic work of structural safety assessment, restoration and repair. Currently the accurate in-situ stress of the pre-stressed concrete structures is very difficultly obtained. This paper present a new ring-hole method with FBG sensors to measure the in-situ stress for a reinforced concrete structure, which based on stress release. The depth of ring-hole, the elasticity modulus of core-like concrete and the comparison of different sensors are studied by two reinforced concrete specimen test. The experimental results show that: (1) The method can effectively and accurately measure the in-situ stress of the concrete structure with the maximum relative error being 8.66%; (2) The stress of concrete will be completely released when the drilling hole depth up to 30mm for 100mm diameter; (3) FBG sensor is more stable than the traditional electrical measurement method.
Forced Vibration Monitoring of Railway Bridges by use of Multiple Long Stroke Shakers
In this paper the potential application of multiple long stroke shakers to railway bridges is suggested to determine the natural frequencies and structural damping coefficients of railway bridges. In practice it is often observed, that the use of conventional monitoring methods, like ambient vibration monitoring, shows limits in the evaluation of realistic structural damping coefficients. By use of the proposed forced monitoring method by application of multiple long stroke shakers a sufficient vertical bridge vibration amplitude is generated and the values of determined structural damping become realistic. Hence, the identified dynamic parameters are ideal suited for the calibration of numerical bridge models. In the years 2016 and 2017 altogether 20 railway bridges with ballast track were measured in both, summer and winter conditions, by use of multiple long strong vibration exciters and the measuring results will be presented in this paper.
Guidelines for Monitoring of Structures in the Offshore Wind Industry
The Offshore Wind Industry is young and dynamic. Little experience on the performance of structures is available. Other than the Offshore Oil and Gas Structures wind generation produces considerable horizontal forces and dynamic actions. It is desirable to learn about real performance cases for future design optimization and save operations. More than 2000 structures have been erected in the German North Sea over the past years. An ambitious monitoring program has been started. In order to guide these a technical committee has been formed to issue a relevant standard. This is the first one worldwide. Information on the draft guideline is provided
Mapping, Assessing and Monitoring Urban Underground Infrastructure
This paper presents results from a series of measurements aimed at mapping, assessing and monitoring underground infrastructure, i.e. the water, sewer, gas, electricity, telecommunications and other supply lines that are vital to modern society. Most of this infrastructure is buried out of sight, in uncertain and highly congested locations and in an aging condition. Mapping, assessing and monitoring this infrastructure can lead to significant improvements in management, repair and growth practices. The sensors include multi-band and multi-static ground penetrating radar (GPR), and underground flow and condition sensing of water systems, including acoustic leak detection, linked by wireless and high-speed fiber-optic networks. Ground penetrating radar is one method available for locating underground utilities, but becomes challenging in urban environments due to the congestion of piping and difficulties with GPS-denied position registration. Ongoing efforts to overcome these challenges with advanced GPR techniques and the integration into a mapping database are presented, including results from field tests with pre and post construction ground truth evaluations. Data telemetry from buried infrastructure for IOT-type monitoring is hampered by the high-attenuation rate for wireless electromagnetic transmission. Results from experiments aimed at low-speed magnetic signaling with potential for high penetration through soils are presented.
Monitoring of Bridges by Optical Strands
A comprehensive solution for the monitoring of the structural behavior of bridges is described, based on continuous strain measures by Optical Strands at a high sampling rate and over a long duration. Optical Strands are strain sensors using fiber optics technology which combine easy set-up, robustness, long life-span and immediate response even for high sampling rates. The system is very relevant for in-situ nondestructive load tests but is also dedicated to long-term monitoring. The monitoring of bridges by Optical Strands involves many challenges in data analysis, due to the large amount of measures at different time scales. By analyzing long-term tendencies as well as short variations of strain recorded for dynamic events such as traffic loads, earthquakes or strong winds, it is possible to get relevant information of many kinds about the structural behavior and the use of the bridge. The continuous monitoring is meant for being integrated in the bridge management in a permanent way. On-the-way upgraded scores and real-time notifications displayed on a general interface which gather many different bridges makes these structures become a new part of the IOT.
Numerical Model for Predicting Carbon Fiber Composite Cable Forces in a Cable-stayed Bridge
Carbon Fiber Composite Cables (CFCC) have high tensile strength, low weight and excellent corrosion resistance. As such they provide a viable replacement to traditional steel cables in many cable-stayed bridges, which may increase the structure lifespan and reduce maintenance requirements. The Penobscot Narrows Cable-Stayed Bridge offered a unique opportunity to develop these technologies. This paper analyzes continuous CFCC structural health monitoring data, and investigates the relationship between the external ambient air temperature and CFCC forces via a numerical model
Performance Monitoring of an Existing Concrete Bridge using Strain Measurement Data
This paper presents the real-time monitoring results of operational RC Kawane Bridge in the suburbs of Mito in Ibaraki Prefecture of Japan. The effects of seasonal changes and earthquake occurrence in addition to the traffic loadings over an interval of six months were evaluated in terms of global and local modal parameters. The response of bridge to ambient and controlled traffic conditions was monitored using accelerometers, conventional strain gauges and long-gauge (FBG) sensors mounted on the underside of the bridge girder. The objectives of the study were to: (1) evaluate the performance of the long-gauge FBG sensors packaged in the laboratory for longterm monitoring of existing bridge structure, and (2) condition assessment of Kawane Bridge using dynamic response measurements due to operational and environmental loading on the bridge. The modal frequencies extracted from the different measurement data agreed. The condition of the bridge was assessed on the basis of the proposed data normalization approach using the normalized modal macrostrain (NMMS) vectors between the target and the reference macro-strain measurements. This procedure, previously deployed to laboratory setups, was an improved modal curvature method with potential to conveniently detect and localize small degrees of damage. Some changes in first modal frequency of the bridge were observed which was mainly due to the variations in environmental parameters. However, no significant changes in the slope of the fit lines were noticed indicating the healthy condition of the monitored bridge. Intensive visual inspection of the Kawane Bridge during the experimentation confirmed the same. The algorithm was verified to be suitable for practical civil SHM applications where variability in response and modal parameters due to measurement noise and environmental influence is often inevitable.
Sequential Importance Sampling Algorithm in Damage Detection of a Cable Stayed Bridge
Damage detection of civil infrastructure has gained significant momentum in the last decade due to aging and being more prone to failure of infrastructure. New advances in sensing technology have made it possible to detect damage in giant infrastructure more effectively. As the size of structure increases, the number of installed sensors on the structure increases significantly. This increase in the number of sensors makes it more challenging to collect and interpret the data. Therefore, utilizing the minimum number of sensors at any time of detection while maintaining an effective monitoring system is of great interest. In this paper, we will develop an innovative on/off sensors methodology using the sequential importance sampling technique to choose the sensors to be turned on at any given time while tracking the potential damage. This method will be used on a cable stayed bridge over Mississippi River called Luling Bridge. The finite element (FE) model of the bridge is developed to introduce the damage at different girder locations in the bridge. This model will be used to show the effectiveness of the proposed sequential importance sampling method in providing effective monitoring with minimal number of turned activated sensors at any given time.
SHM Application to USACE Large Civil Infrastructure: Development of Trunnion Friction Detection Methodology for Radial (Tainter) Gates
Radial, or Tainter, gates are the most commonly used gate type for large, controlled spillways. The failure of a Tainter gate has the potential to be catastrophic, and could include loss of life, flooding of homes and businesses, significant ecological impacts, and unscheduled repair/replacement at premium cost. In 1995, the Folsom Dam failed suddenly due to corrosion that developed at the trunnion pin-hub interface, creating rotational friction during operation. This failure raised awareness within the US Army Corps of Engineers (USACE) regarding the need to advance the state of design, inspection, and maintenance planning related to this potential hazard. In direct response to concern expressed by numerous USACE districts, the Engineer Research and Development Center (ERDC) initiated a research project to develop an automated detection system that uses real-time structural data collected through Structural Health Monitoring techniques, to indicate the presence of trunnion friction. In this paper, a methodology developed to measure the effective level of friction at a Tainter gate trunnion hub using a minimal number of strain gages will be described. The ability to determine, in real-time, the level of trunnion friction in a Tainter gate trunnion could provide an operator vital information, which could be used to avoid a catastrophic failure. As part of this research effort, a 1/5th-scale numerical model was developed and a scaled physical model was designed and fabricated, using the Big Cliff Dam Tainter gates as the basis of design. The physical model was instrumented with a strategically placed suite of sensors to collect data, and the proposed feature detection strategy was shown to be successful. The paper will provide details of the lab test and field deployment, including a description of the setup, instrumentation strategy, and conclusions. The results of this research would be of interest to the structural health monitoring community, and to design and maintenance engineers responsible for large civil infrastructure
Stiffness Identification of Stiffness Identification of High-rise Buildings via Subspace and Inverse-mode Methods
A system identification (SI) problem of high-rise buildings is investigated. The shear and bending stiffnesses of a shear-bending model (SB model) representing the high-rise buildings are identified via the subspace and inverse-mode methods. Since the inverse-mode method can provide the shear and bending stiffnesses of the SB model directly from the lowest mode of horizontal displacements and floor rotation angles, the lowest mode of the objective building is identified first by using the subspace method. Identification of the lowest mode is performed by using the amplitude of transfer functions derived in the subspace method. Considering the resolution in measuring the floor rotation angles in lower stories, floor rotation angles in most stories are predicted from the floor rotation angle at the top floor. An empirical estimation equation of floor rotation angles is proposed by investigating those for various building models. From the viewpoint of the application of the present SI method to the practical situation, a non-simultaneous measurement system is also proposed. In order to investigate the reliability and accuracy of the proposed SI method, a 10-story building frame subjected to micro-tremor is examined.
Study on the Monitoring Method of Windinduced Response of Transmission Tower Structure Based on Dynamic Analysis
It is important to study the wind disaster reduction method of transmission tower structure in typhoon prone coastal areas. In order to ensure the safety of the transmission tower structure and its power line under the typhoon wind load, it is proposed that the transmission tower structure should be monitored using bridge health monitoring method. The monitoring items including the wind power, wind speed and dynamic response of tower structure. Firstly, the finite element model of transmission tower structure system was built to make the dynamic and time history analysis under the simulated typhoon wind load; Secondly, with the result from analysis, the vibration acceleration, vibration displacement and time history curve of each part of the tower are analyzed, and the dynamic response rule of the tower under the action of vector wind load is obtained. Based on the finite element analysis results, the parts which had the large dynamic response are selected to arrange as measuring points of tower structure. The selection method of monitoring sensor is proposed according to the dynamic monitoring requirements. At last the wind monitoring system of transmission tower was presented. In the typhoon prone season, the dynamic monitoring system can be used to collect real-time response data of the tower, which can provide the basic data for the study of the wind disaster reduction for tower structure.
Towards an Automated Damage Detection System for Miter Gates on Navigation Locks: Detecting Boundary Condition Degradation
Navigation locks are critical infrastructure components, and their closure for maintenance and repair can have significant impacts on the global economy. The current state of inspection and monitoring of lock components is generally to close the lock and perform a visual inspection. While structural health monitoring (SHM) of navigation locks is gaining acceptance, automation of the SHM process is lacking. This paper reports on efforts to develop an automated damage detection system for miter gates of navigation locks. The study focuses on using strain gage measurements to identify the redistribution of load throughout lock gates in the presence of damage. To eliminate the environmental variability in the data, a new damage sensitive feature is introduced, termed here as “slopeâ€ and defined as the derivative of the strain with respect to the water levels in the lock chamber. The slopes form a new, stationary time series effectively purged of environmental effects. Principal Component Analysis (PCA), a method of analyzing multivariate, stationary time series, is then used to detect significant changes in the statistics of slopes as an indication of damage. To validate the approach, damage is simulated in a finite element model, and the resulting changes in strain from the finite element model are superimposed on the measured data. The results demonstrate the potential of the proposed approach for detecting damage in navigational lock gates.
Application of Interface Waves for Damage Detection in Laminated Composite Bends
This research investigates a novel ultrasonic method using guided interface waves for the inspection of curved hybrid laminate composite structures. Most composite airframe components, such as skin – stringer and web - flange attachment of beams and channels, form structural elements which are known as Noodles. Noodles or radius fillers are used to fill-out the radius bend of curved structures. Noodles are constructed from composites with orientations different from the primary laminated structure. Noodles are vulnerable to many forms of damages and flaws. Noodles are also located in areas that might not be very convenient for inspection by typical nondestructive methods. In this research, the propagation of ultrasonic guided interface waves in a skin – stringer configuration with noddle is studied. The surface acoustic wave on the curved outer surface is modeled. The intention is to identify effective guided waves for the structural curvature radius inspection. The guided wave propagation path along the bondline of skin – stringer and noodle area is simulated numerically by finite element model. Typical damage scenarios in the noodle area are modeled. The ultrasonic interface wave behavior is studied for several cases to characterize noodle damages. The attenuation and scattering of wave-front suggest the interface wave can detect several damages at certain frequency range, when the wavelength is compatible with the bend radius of the structure. Also the location of damage in the noodle area can be found by reflection sources from different defect sizes. This study can be used for a generalized solution of noodle inspection problem and in connection with examination of curved surface as part of the development of a structural health monitoring system.
Baseline-Subtraction-Free (BSF) Damage-scattered Wave Extraction for Stiffened Isotropic Plates
Lamb waves enable long distance inspection of structures for health monitoring purposes. However, this capability is diminished when applied to complex structures where damage-scattered waves are often buried by scattering from various structural components or boundaries in the time-space domain. Here, a baseline-subtraction-free (BSF) inspection concept based on the Radon transform (RT) is proposed to identify and separate these scattered waves from those scattered by damage. The received time-space domain signals can be converted into the Radon domain, in which the scattered signals from structural components are suppressed into relatively small regions such that damage-scattered signals can be identified and extracted. In this study, a piezoelectric wafer and a linear scan via laser Doppler vibrometer (LDV) were used to excite and acquire the Lamb-wave signals in an aluminum plate with multiple stiffeners. Linear and inverse linear Radon transform algorithms were applied to the direct measurements. The results demonstrate the effectiveness of the Radon transform as a reliable extraction tool for damage-scattered waves in a stiffened aluminum plate and also suggest the possibility of generalizing this technique for application to a wide variety of complex, large-area structures.
Bio-robotic ROV Design for Pipe-work Opto-sensorial Safety-inspections
Among the various chemical and mechanical engineering form of assembly, tube and pipe-work are very common and important. Since it is important to ensure that welding and connections are sound it is not surprising that many remote operated systems have been developed to carry out integrity inspections. Internal sensorial inspection using remotely operated vehicle (ROV) have been developed especially thanks to the recent progress of miniaturization and cost reduction. But applications are complicated by materials, geometries, environment challenges and the industry is continuously looking at new solutions that are flexible and affordable. In this paper, we propose a new robot with wheel grip designs that combined with different movement mechanisms can bring novel advantages, for example, helping inspecting pipework systems which are more delicate and perhaps cannot take high axial loads or for irregular pipe transitions. Tests have been carried out on a wheel system where the tyre is made up by silicon with a series of row of three suction pads that are forced on the internal surface of Perspex pipe as the wheel rotates pushed by a limited force.
Identification of the Location and Level of Loosening in a Multi-bolt Structure using Nonlinear Ultrasound
This work evaluated a nonlinear method for in-situ structural health monitoring (SHM) of the loosened state of a multi-bolted structure. The aim was to assess a bolted structure with an array of piezoelectric transducers and determine which bolt had loosened and the extent of loosening by evaluating nonlinear ultrasound responses. Linear methods generally focus on traditional methods that evaluated the change of the damping curve and signal energy as the torque on the bolts of a structure changes. Whereas the nonlinear methods used evaluated the second harmonic and modulated responses using a Nonlinear Acoustic Moments (NAM) method coupled with a frequency selective process based on the cross correlation of the nonlinear responses at various loosened bolt states. The ultrasound excitation method relied on modulation between a continuous single frequency signal and pump sweep signal (multi-frequency), which generated a broad band nonlinear response. By exciting over a broad range of frequencies, probability of bolt specific frequency excitation and thus determination was improved. The nonlinear techniques showed good ability to determine the bolt specific frequency for each bolt and the extent of loosening.
Damage Diagnosis in Composite Materials under Applied Load with Guided Waves-based SHM
Structural health monitoring (SHM) of composite structures provides an approach for fully utilizing the benefits of composite materials while providing a strategy for monitoring their complex damage mechanics in real-time. Application of SHM requires not only the ability to localize, classify, and quantify damage in a structure, but also to compensate for changing environmental and operational conditions. This work will focus on one such problem, specifically identifying the presence of delamination in composites while the material is under applied load. An experimental series was conducted testing composite coupons under tension-tension fatigue in order to cause damage to gradually propagate through the samples. Guided wave signals, propagated through the composite samples using piezoelectric transducers, and verification X-ray images were collected at progressive stages and different applied load levels during testing. By analyzing ultrasonic signal changes from the SHM system when experiencing effects from varying levels of damage severity and applied load the goal is to identify delamination in a signal propagation path. Signal features extracted from the guided wave data, such as amplitude, velocity, and energy content, are used to identify delamination. Utilizing combinations of signal features as prediction variables for statistical modeling, decision trees are grown to create a multi-layer model which uses these signal changes in order to predict delamination regardless of the operational effects also influencing the guided wave SHM system
Data-driven Pattern Recognition Model Employing Auditory Receptors for Human-based Structural Health Monitoring System
A quintessential conceptual element common to most Structural Health Monitoring (SHM) systems is the use of non-destructive methods and technologies to allow for the uninterrupted and efficient monitoring of structural damages. Recent advancements pertaining to the logistical underpinnings of novel damage detection SHM techniques have caused a shift from mathematical modeling of structures to pattern recognition algorithms encompassing both supervised and unsupervised learning methods. This work draws inspiration from current progressions in the field of neuroscience to incorporate the human brain in performing supervised pattern recognition, whereby the initial (i.e. damaged or undamaged) state of the structure is of prior knowledge. In this context, a framework for damage detection relating to shear-building structures has been developed for practical implementation in response to a range of different damage circumstances. The proposed framework uses human ears as auditory receptors to communicate received data to the human brain for interpretation. Two distinct audio signals are derived from the modal analysis of the structure. Induced audible signals are exposed to human subjects, and through consistent training, the ultimate ability of subjects to determine the state of structures is evaluated through training-test schemes for gathered responses. The results obtained are then contrasted with those obtained through the use of specific Machine Learning (ML) algorithms. Initial results involving direct human perception have thus far revealed a noteworthy impact effected by these assessments on understanding the behavior of structures. Possible areas of future growth may also be exploitable if other receptors (for instance, touch receptors as vehicles to transfer data to the human brain through vibrotactile patterns) are used in similar pattern recognition SHM systems
Defect Imaging on CFRP and Honeycomb Composite Structures by Guided Waves Generated and Detected by a Sparse PZT Array
Sandwich honeycomb structures (aluminum core bonded to Carbon Fiber Reinforced Polymer (CFRP) sheets on either side) are widely employed in the aerospace industry for their high strength to mass ratio. However, they might be subjected to damages such as delaminations of the composite sheets or debondings between the face sheets and the core due to impacts or thermo-mechanical aging. In order to reduce maintaining costs and extend the service time, Guided Waves (GW) based Structural Health Monitoring (SHM) systems are considered an adequate solution. Indeed, GW propagate over long distances and exhibit low attenuation, thus allowing to monitor wide areas with a limited number of sensors. Defect imaging on CFRP composites and honeycomb composite structures using both Delay-And-Sum [1] and correlation-based algorithm Excitelet [2] is presented in this communication. A machine learning algorithm is finally implemented in order to automatically identify and isolate defects on a given cartography map. The machine learning algorithm is trained on an experimental database of false and positive results obtained on representative specimens.
Determining the Deformed Shape of Beams using the Conjugate Beam Method and Strain Measurements
Determining the deformed shape of a structure is an important aim of Structural Health Monitoring (SHM), as it can indicate damage or abnormalities in the behavior of structures. Moreover, the deformed shape is a parameter that is present in all beamlike structures. The deformed shape can be described as the shape of the centroid line of the structure after deformation, and is a result of loading patterns on the beam. Currently, options for determining the deformed shape of the structure include direct external measurement, for example, with radar, lasers, camera imaging, or GPS, or by indirect derivation from internally measureable parameters, such as strain and curvature. Accurate direct long-term monitoring is frequently difficult to achieve mainly due to the effects of environmental factors. Certain indirect methods of analyzing the deformed shape, such as the double integration of curvature, require specific knowledge of a structure’s boundary conditions. This paper applies and analyzes another indirect monitoring technique for determining the deformed shape: the conjugate beam method. The main aim of this study is to evaluate the errors in using the conjugate beam method for determining the deformed shape. The error in this method depends upon the loading configuration, curvature magnitude, and quantity of sensors on the structure. Finally, the conjugate beam method is validated using laboratory test data and data collected from a real structure.
Enhanced Empirical Wavelet Transform with Application to Rolling Bearings Fault Diagnosis
The Empirical Wavelet Transform (EWT) is a novel method for analyzing the multi-component signals by constructing an adaptive filter bank. Though it is an effective tool to identify the signal components, it has drawback in dealing with some noisy and non-stationary signals due to its coarse spectrum segmentation. To target this problem, an enhanced EWT (EEWT) is proposed to decompose the signal into empirical modes with physical meanings. This method ameliorates the drawback of EWT by taking the spectrum shape of the processed signal into account. It improves the segmentation process by adopting the envelop approach based on the order statistics filter (OSF) and applying three criteria to pick out useful peaks. In this work, the envelope spectrums of extracted empirical modes are applied to the rolling bearing fault diagnosis. Because the EEWT can decompose vibration signal into a set of mono-components, fault features are found clearly in the envelop spectrum. The effectiveness of the proposed method is verified by a simulated signal and a real signal captured from a test rig.
Impact Localization using Sparse PWAS Networks and Wavelet Transform
In this work, an impact experiment on an aluminum plate is implemented to localize the impact points. A small steel ball is dropped on an Aluminum plate to generate acoustic emission signals acquired by 9 PWAS transducers distributed across the plate. Two possible options exist for determining the time of flight (TOF). The first option is determining the TOF of the starting point of a signal (method 1). The second option is determining the TOF at the location of the maximum signal value (method 2). In our work, both method 1 and method 2 are examined comparatively. Based on the number of PWAS transducers, six networks are created to localize the impact points separately. The first network (Network 1) has four PWAS transducers (the minimum number of PWAS transducers to localize an impact point on a plate with unknown wave velocity). The second one is created by adding the fifth PWAS transducer to the Network 1. The same procedure designs the other networks. The effect of increasing the number of PWAS transducers on the percentage error of the calculated location of an impact point is examined. The continuous wavelet transform (CWT) of the signals are calculated with AGU Vallen wavelet program to determine the TOF of the signals. The results show that the method 1 of determination the TOF has fewer percentage errors than the method 2. The percentage errors of calculated locations of impact points decrease with increasing the number of used PWAS transducers.
Model-assisted Assessment of Damage Detection and Localization using Guided Wave-based Imaging Techniques
In this paper, a model-assisted approach is proposed for assessing the ability of guided wave (GW)-based Structural Health Monitoring (SHM) imaging techniques to detect and localize a damage on a structure. A damage identification metric (DIM) is determined from the damage index (DI) image produced by the SHM system when a damage is present on the structure. Several damage sizes and positions are considered, thus producing various values of the DIM. The probability of detection (POD) curve is then calculated using the â vs. a approach well established in the nondestructive testing (NDT) community. For assessing the localization performance of the system, the probability of localization (POL) curve is derived from the ratio of defects correctly localized to the total number of defects, based on the calculation of the absolute error of localization (AEL). The approach is applied for assessing the performance of an actual SHM system composed of three piezoelectric actuators bonded on an aluminum plate. A good agreement is observed between POD and POL curves obtained experimentally and using a finite element model (FEM) of the system. The model-assisted approach is finally used to predict the performance of the system assuming a variation of temperature between the baseline acquisition and the detection test.
NDT Approaches to Optimize Acoustics Based SHM Systems for Anisotropic Composite Structures
Appropriate and reliable monitoring with SHM systems providing a high probability of detection is a challenge in itself. Compared to traditional nondestructive testing (NDT), where a near to endless sampling series can be performed, the sensor pattern for a SHM system is fixed and as such also sampling is limited with regard to the fixed transducer positions. Finding the optimum position of a SHM system’s transducer network is therefore crucial and can only be reasonably achieved through simulation. This situation of appropriateness of the SHM system is aggravated when it comes to structures made of anisotropic materials such as fibre-reinforced materials where the elastic constants may not be known per se. An approach to alleviate this problem is therefore presented. It is based on using novel techniques developed in NDT which have to be used prior to configuring an SHM system optimized to reliably detect a tolerable damage in the composite structure considered. This starts by applying conventional ultrasonic testing, possibly on the basis of phased array transducers and to sample data in a way that the materials’ effective stiffness constants describing anisotropy are based on the full matrix capture (or sampling phased array) and reverse phase matching methods, which allow those constants to be determined on an iterative basis. Once these constants are determined a guided wave based simulation can start which allows wave patterns in an anisotropic materials environment to be generated for undamaged and damaged conditions. Looking then either at non-linear dynamic properties within the material or the difference in guided wave signals between an undamaged and a damaged condition of a structure for a given actuation pattern will allow sensor locations to be determined at the locations where the difference in signals between the undamaged and the damaged condition becomes maximum for a tolerable damage defined. Based on these simulation results the sensor pattern for the SHM system considered to be optimum is determined. The approach is illustrated on different examples performed.
Remote Mechanical Vibration Sensing: A Comparison Between CW-Doppler Radar and Laser-Doppler Vibrometer Measurements
Vibration-based structural health monitoring (SHM) systems mainly use accelerometers or strain gauges for measuring the dynamic response of the structure. However, these types of sensors require a direct contact which limits their applicability in many applications like for example the analysis of rotating machinery. One opportunity to measure the electrodynamic vibration from a distance of several meters is to use Laser-Doppler vibrometers (LDV). However, due to the high price and the complexity of such LDV-systems it is not feasible to apply them in permanent sensor installations. A possible solution is the usage of radar sensor devices that can be used for remote mechanical vibration sensing either in a continuous wave (CW) or frequency-modulated continuous wave (FMCW) mode. A cheap production of the radar devices – as demonstrated by the automotive sector – makes their permanent application also feasible in SHM-applications. The goal of this paper is to compare – for the first time – two CW-Doppler radar sensors at 24 GHz and 94.5 GHz with a Laser Doppler vibrometer. In this paper, we have measured the performance of both Doppler radars and LDV simultaneously, and the result is validated using a force sensor which is directly attached to the tested specimen. Experimental outputs are shown and it is verified that there is a good agreement in results between all the four sensor types. Also, various harmonics are observed which are probably due to the artifacts. This experiment shows the potential of replacement of LDV with the low-cost Doppler radar system in SHM-applications.
Signal-based Versus Nonlinear Model-based Damage Sensitive Features for Delamination Quantification in CFRP Composites
Structural health monitoring (SHM) is an emerging technology designed to automate the inspection process undertaken to assess the health condition of structures. The SHM process is classically decomposed into four sequential steps: detection, localization, classification, and quantification. In this paper, SHM quantification step is addressed. Particularly, we approach delamination quantification as a classification problem whereby each class corresponds to a certain damage extent. Starting from the assumption that damage causes a structure to exhibit nonlinear response, we investigate whether the use of nonlinear model based features increases classification performance. A support Vector Machine (SVM) is used to perform multi-class classification task. Two types of features are used to feed the SVM algorithm: Signal Based Features (SBF) and Nonlinear Model Based Features (NMBF). SBF are rooted in a direct use of response signals and do not consider any underlying model of the test structure. NMBF are computed based on parallel Hammerstein models which are identified with an Exponential Sine Sweep (ESS) signal. Dimensionality reduction of features vector using Principal Component Analysis (PCA) is also carried out in order to find out if it allows robustifying the quantification process suggested in this work. Experimental results on Carbon Fiber Reinforced Polymer (CFRP) composite plates equipped with piezoelectric elements and containing various delamination severities are considered for demonstration. Delamination-type damage is introduced into samples in a calibrated way using Laser Shock Wave Technique (LSWT) and more particularly symmetrical laser shock configuration. LSWT is chosen as an alternative to conventional damage generation techniques such as conventional impacts and Teflon inserts since it allows for a better calibration of damage in type, depth and size. Results show that by introducing NMBF, classification performance is improved. Furthermore, PCA allows for higher recognition rates while reducing features vector dimension.
Statistical Based Features Vector for Skin-stringer Debonding Detection
In composite aeronautical structures at the stiffener terminations, the carried loads are transferred to the skin, making vital the design of the run-out region. Hence, improved structural health monitoring methods are required. The observations showed that failure initiates in the flange close to the tip in many operational cases, causing an almost full disconnection from the skin. This paper presents a simple methodology for skin-stringer de-bonding detection by using distributed fibre optic sensors and first order statistics. The proposed technique is based on the deviation comparison between a baseline set of measurements (irrespectively of the healthy state) and the current structural response. The assessed datasets are then processed in order to provide normalbased statistical features, an appropriate threshold limit and a damage index for each sensor. The algorithm points then out the positions where the possibility of the existence of a damage is maximum.
Transfer Impedance Approach to Damage Detection and Localization Based on RAPID Imaging Algorithm
One of the ideas for structural health monitoring (SHM) systems built is based on analysis of small displacements propagation excited in the element by a network of PZT piezoelectric actuators. Structural damages can result in observable changes of the signal generated by the network sensors, due to elastic wave interaction with damage. The electromechanical impedance (EMI) method approach to SHM, harmonic excitation of PZT is used, thus steady elastic waves are excited in the structure. The signal can be gather in the pulse – echo scheme, i.e. when single transducer is used both as a actuator and the receiver of waves, as well in the pitch – catch scheme, when a pair of transducers, the generator and the sensor, are used. For the latter EMI approach, the term Transfer Impedance Approach is sometimes used. In the paper, an approach for damage detection and localization with use of network of PZT sensors excited with harmonic signals in broad frequency spectrum is presented. In particular, some signal characteristics – called Damage Indices (DI’s) used for structure assessment are proposed and their properties are discussed. The DI’s have the property to assess the location a damage with respect to a single sensing path, formed by a pair of transducers, i.e. the generator and the receiver. Finally, the RAPID algorithm for damage localization with use of the information from all of the network sensing paths is applied.
Use of Time-frequency Damage Sensitive Features for Structural Damage Diagnosis
This paper presents a recent shake table experiment on a 4-story steel frame with predictable damage. The yielding of column in the first story was produced to represent damage and the deterioration changed the structural properties. Using acceleration measurement, the damage can be obtained and compared with visual inspection of the structures. In addition, a structural damage detection algorithm based on time-frequency representation was validated based on the experimental data
Electromechanical Impedance-based Damage Identification Enhancement using Integrated Bistable and Adaptive Piezoelectric Circuitry
The electromechanical impedance-based damage identification methods using piezoelectric transducers have shown excellent potential in identifying small-sized structural defects, while maintaining simplicity in implementation. The available independent impedance measurement data sets, however, are generally far fewer than the number of required system parameters, which results in a highly underdetermined inverse problem for damage identification. This numerical ill-conditioning severely undermines the reliability of damage prediction since the inverse solution becomes extremely sensitive to even small amount of error in the measurement data, especially in practical implementations where the response anomaly induced by small-sized damages may be easily buried in noise. Furthermore, the response around resonance peaks where the damage effect is most pronounced could be suppressed by structural damping in high frequency interrogation to detect small defects. To address such issues and advance the state of the art, this research explores a novel method that accurately measures the damage-induced impedance variations under noise influences and fundamentally improves the underdetermined inverse problem to identify the location and severity of small damages. The core of this new approach is to exploit the strongly nonlinear bifurcation phenomena in bistable electrical circuits coupled with piezoelectric transducers for evaluating impedance changes. By utilizing the voltage measured from a piezoelectric transducer as the input voltage to the bistable circuit, the structural response and related impedance change induced by damage can be assessed by activating bifurcations in the circuit that leads to dramatic change in the output voltage level. In this study, an array of bistable circuits having consecutively different input gains is systematically designed to determine the impedance variations with highly improved resolution by monitoring whether each circuit exhibits intra- or inter-well response. The number of impedance variation measurements is then greatly increased with respect to the same damage by utilizing an adaptive piezoelectric circuitry which enables us to alter the dynamics of the electromechanically coupled system. The enriched data set is utilized to fundamentally improve the underdetermined inverse problem for damage identification. A series of analyses on a beam structure using spectral element model has confirmed that the proposed methodology significantly enhances the accuracy and reliability of predicting the location and severity of small damages under noise influences.
Electromechanical Impedance Sensitivity Analysis Based Structural Health Monitoring using Sub-frequency for Compensating Temperature Effects
Environmental temperature changes significantly affect the piezoelectric impedance spectrum signal, which may lead to damage misjudgment and damage omission if effective compensation measures are not taken. This article carries on the impedance spectrum signal of temperature sensitivity analysis, which shows a significant difference in the sensitivity with temperature of different frequency ranges. The cross-correlation coefficient is taken to quantitatively describe the impedance spectrum signal drift. The method of sub-frequency compensation is used to correct the impedance spectrum signal drift. The bolt looseness damage identification experiment under the influence of temperature using piezoelectric impedance method is carried out, and the result shows the effectiveness of sub-frequency compensation.
Model-based Evaluation of Electro-mechanical Impedance Measurements for Detection and Size Identification of Face Layer Debondings in Sandwich Panels
The present work investigates, based on simple beam models, the well-established vibration based electro-mechanical impedance method as a potential Structural Health Monitoring (SHM) method for the monitoring of debonding in sandwich structures. The Finite Element Method (FEM) is used to analyze the response of a simple beam on different linear and non-linear elastic foundations, representing a sandwich face layer debonding, to a harmonic excitation. First, a finite element based approach to study nonlinear steady-state vibrations of structures is developed and discussed by analytical considerations. Consecutively, the numerically calculated structural responses are presented in detail and their non-linear features are revealed. Finally, the model based findings are discussed regarding their potential to help monitoring damages like, e.g., sandwich face layer debonding that provoke a non-linear response to a harmonic excitation and a short outlook of related future research is given.
Optimum Reduction of Electrodes Needed for Delamination Identification using Electrical Resistance Tomography
Damage in Carbon Fiber Reinforced Polymer (CFRP) composites due to cracking modifies the internal electrical conductivity of the composite material. Electrical Resistance Tomography (ERT) is a non-destructive evaluation (NDE) technique that takes advantage of electrical conductivity changes to determine the extent of damage. Implementation of ERT for damage identification in CFRP composites requires the optimal selection of the sensing sites for accurate results. The present work uses an effective independence (EI) measure for selecting the minimum set of measurements among all possible for ERT damage identification. The electrical potential field in two CFRP laminate layups with 14 electrodes is calculated using finite element analyses (FEA) for a set of specified delamination cases that span the space of possible damage states. Three EI reduction strategies are applied on two different sets of measurements for each layup. The effectiveness of EI reduction is demonstrated by comparing the inverse identification results of delamination cases for the full and the reduced sets after applying the strategies. This work shows that the EI measure optimally reduces electrode and electrode combinations in ERT based damage identification
Study of Adhesive Bonds by Mechanical Tests, Ultrasounds and Electromechanical Impedance Method
The paper deals with adhesive bonding of CFRP plates. Adhesive bonding of composites is considered as good alternative to riveting, provided that good tools are available to assess the bond quality after manufacturing and during the lifetime of structures. The performance of an adhesive bond depends on the physico-chemical properties of the adherent surfaces. A weak bond can be caused by improper surface preparation before bonding. In the research reported herein, modifications of one of the surfaces to be bonded were considered. These modifications are realized using chemical contamination or thermal treatment. The influence of surface modification on bond performance was investigated by electromechanical impedance (EMI) method, ultrasounds and mechanical tests. In the EMI method, a piezoelectric transducer was mounted at the middle of each sample surface. Using an impedance analyzer the electrical parameters were measured for wide frequency band. Due to piezoelectric effect the electrical response of a piezoelectric transducer is related to mechanical response of the sample to which the transducers is attached. The ultrasonic testing (UT) was focused on C-scan analysis taking into consideration the amplitude and time of flight of the signals. A probe with 10 MHz central frequency was used, to highlight different effects on the responses. Mode-I and Mode-II fracture toughness tests were conducted aiming to gain knowledge on the influence of surface modification on the bond strength. The performed investigation showed that bond quality is influenced by the bond modifications.
A Novel Heatsink for Thermo-Electric Power Harvesting of Structural Health Monitoring Systems
Recent research in energy harvesting is leading to the possibility of developing self-powered structural health monitoring (SHM) systems for aerospace applications. Particularly, temperature gradients originated at different locations of the aircraft can be converted into electric current by using thermoelectric generators (TEGs). TEGs are a suitable option for power harvesting since they (i) can be easily mounted on aircraft components, (ii) are lightweight, (iii) do not contain movable parts and (iv) provide high-energy conversion. TEGs are generally combined with thermal diffusion systems (heatsinks) to dissipate heat from a high temperature surface to the ambient air. The present study investigates the design, manufacturing and testing of a novel air-cooled heatsink used to increase temperature gradients on the surfaces of a commercial TEG element and, in turn, the power generation. In particular, a novel heatsink geometry and hybrid material configuration are analysed to enhance the heat spread in the power harvesting system in order to further improve the TEG performance. Numerical finite element simulations and experimental tests are carried out to assess the characteristics of the novel heatsink in combination with a commercial TEG device. Experimental results reveal that the proposed thermal diffusion system provides higher level of performance in comparison with traditional fin array heatsinks. Moreover, considering 100°C and 25°C as heat source and ambient temperature, respectively, the presented heatsink-TEG arrangement produces enough energy to power an ultrasound SHM system for aerospace applications (output power over 30 mW).
An Enhanced Guided Wave-Gaussian Mixture Model for Aircraft Structural Damage Monitoring under Varying Environmental and Operational Conditions
During the past two decades, aircraft Structural Health Monitoring (SHM) technology has gradually turned from fundamental research and laboratorial validations to engineering-oriented developments. However, the process of the transition from research to application has been rather slow and the SHM application to real in-service aircraft structures is barely reported. One of the main application obstacle of the SHM application to real in-service aircraft structures is the problem of reliable damage monitoring under aircraft in-service environmental and operational conditions (referred to as time-varying conditions). Several methods have been proposed to deal with this problem but limitations remain. In this paper, an enhanced Guided Wave-Gaussian Mixture Model (GW-GMM) based damage monitoring method is studied. It can be used on-line without any structural mechanical model or priori knowledge of damage under time-varying conditions. With this method, a baseline GW-GMM is constructed first based on the GW features obtained under time-varying conditions when the structure is in healthy state. When a new GW feature is obtained during an on-line damage monitoring process, the GW-GMM is updated by an enhanced update mechanism including dynamic learning and Gaussian components split-merge. The mixture probability structure of the GW-GMM and the number of Gaussian components can be optimized adaptively. Finally, a Probability Damage Index is used to measure the variation degree between the baseline GW-GMM and the on-line GW-GMM to reveal the weak cumulative variation trend induced by damage of the GW-GMM so as to increase the reliability of damage evaluation. The method is validated in a full-scale aircraft fatigue test and the results indicate that the reliable crack propagation monitoring of the right landing gear spar under the fatigue load condition is achieved
Principal Component Analysis and Artificial Neural Network Framework for Damage Detection Strategy under Varying Operational Loading Conditions
One of the key challenges in developing a robust damage detection system for aircrafts in service is the capability to discriminate the effects of operational and environmental variations from damage-sensitive features. This work aims to shed some light in normalizing the vibration test data under the effects of operational loading variables analyzed in frequency response function (FRF) by means of standard Principal Component Analysis (PCA) and fused in with Artificial Neural Network (ANN) framework. PCA is applied in the vibration data set acquired from a wing box structure with an attached liquid tank in aim to reduce the dimensionalities as well as for visualization technique. The step is essential in interest to examine the correlation between both load and damage parameters with the key point of damage and load identification. To develop a robust damage novelty detector technique, the PCA is incorporated with the forward feed two-layer Artificial Neural Network (ANN) with an optimum numbers of perceptron. The output from incorporating both PCA and twolayer ANN has shown encouraging and positive novelty damage detection results under varying load and damage parameters.
Thermal Effect Identification and Bridge Damage Disclosure by using Blind Source Separation Method
In this paper, blind source separation idea is employed to identify abnormal variations in time-history sensor measurements, which can be the indication of structural damage inside the bridge. Since the measurements are the mixed structural responses due to the complicated surroundings, environmental impact should not be ignored any more. The reason is environmental variations can not only change structural properties but also mask the response changes that indicate structural performance degradation. As it is difficult to procure the complex loading surroundings, the proposed method, thus, employs principal component analysis and independent component analysis method to uncover temperature variations and subsequently detect damage, without any prior information of the loading conditions and pre knowledge of structural physical models. The principal component analysis is employed as an assistance to reveal the number of latent loading conditions. Thereafter, the independent component analysis, one of the popular solution for blind separation stream, is applied on target sensor set to extract temperatureinduced variations and identify damage. The simulated damage scenarios are introduced into the aluminium bridge, which is a scaled model of a Japanese railway bridge. The results obtained from this case study illustrate that not only the temperature effects can be identified, but also anomalous variations can be uncovered along with their position.
A Categorical Approach Towards Metamodeling Cyber-physical Systems
Cyber-physical systems, orchestrating computing, networking, and physical processes, are coupled, hybrid systems characterized by heterogeneous, interconnected subsystems. In civil engineering, the most common applications of cyber-physical systems are associated with structural health monitoring (SHM) and structural control. Given the complexity and heterogeneity of cyber-physical systems designed for structural health monitoring and control applications, it is hardly possible to reliably assess the quality of a cyber-physical system (CPS), which is needed for increased availability and for identifying potential sources of faults in a CPS. This conceptual paper proposes a metamodeling approach that can be used as a basis to model cyber-physical systems in order to assess the CPS quality based on category theory. Category theory is a mathematical theory that allows formalizing concepts of high-level abstractions on a meta level. Using the CPS metamodeling approach presented in this paper, each subsystem of a CPS can be modeled and assessed individually, entailing a holistic quality assessment of a CPS and its subsystems. However, since this paper is conceptual, it is intended to devote additional future research efforts to metamodeling and assessing cyber-physical systems, achieving a complete conceptual modeling framework for cyber-physical systems applied in structural health monitoring and control.
A New Ultrasonic Immersion Technique to Retrieve Anisotropic Stiffness Matrix for Dispersion Curves Algorithms
Dispersion curve algorithms require the material properties of the anisotropic medium in which the ultrasonic guided-waves (GW) propagate as input. These material properties are currently obtained through ASTM standard mechanical testing procedures. Practice shows that researchers limit themselves to determining the E11 , E22 , G12 and ν12 , while the other engineering constants are then based on assumptions such as: E33 = E22 . The engineering constants are subsequently converted to the stiffness matrix using the relation between the elastic constants. The calculated stiffness matrix is used as input in the dispersion curve algorithm, however, the predicted results may vary significantly from those obtained experimentally due to the assumption. A more accurate method of determining the stiffness matrix is desired. In this research, the stiffness matrix components are determined non-destructively using a modified through-transmission ultrasonic immersion technique. Based on the existence of planes of symmetry within an orthotropic material, the stiffness matrix retrieval process was divided into parts to reduce the complexity of the process and increase the accuracy of the solution. As last, The group velocity dispersion curves are calculated analytically with the material properties from both the ASTM standards and the ultrasonic immersion technique. Both predicted results were compared to experimentally obtained velocities reported in the literature.
A Simplified Treed Gaussian Process Approach to the Modelling of Bridge Data for Structural Health Monitoring
Data-based modelling in the presence of uncertainties is a key challenge for Structure Health Monitoring (SHM) practitioners. In the scenario of applying damage detection methods using time series analysis, many of the approaches are based on Statistical Process Control (SPC). The inherent limitation within a SPC-based method is the restriction to stationary data. However, given the inevitable presence of uncertainties from the environment, stationary data acquisition is not guaranteed. To explore the potential relations behind the data while accommodating the issue of non-stationary behaviour, a Treed Gaussian Process (TGP) can be brought into action. TGP models have been shown to be successful in tackling non-stationary regression problems effectively, and have been adopted and applied across numerous research fields including SHM. However, a key limitation that has been found is the computational complexity of these models. This paper introduces a simplified TGP model that is distinct from previous formulations in being based on a different and less complex statistical model. The simplified version presented here reduces the complexity by removing and replacing parameters from previous formulations, as well as sampling and evaluating using a different criterion. The objective for the algorithm is to maintain accuracy and effectiveness while reducing the computational cost of implementation. The simplified algorithm is demonstrated here for two case studies. First, a benchmark comparison is made using a well-known motorcycle accident dataset. Secondly, the algorithms will be compared using experimental data from the well-known Z24 bridge SHM dataset.
Characterizations of Fundamental SH Wave Generation using a Fully Coupled Dynamic Model
Fundamental shear horizontal (SH0) waves in plate-like structures are of great importance in structural health monitoring (SHM) applications due to its unique nondispersive nature. The generation and the reception of the SH0 waves using piezoelectrics, however, is always a challenge. In this study, a dynamic model on the shear horizontal (SH) wave generation is proposed based on the continuum mechanics theory, in which the full dynamic coupling between a PZT actuator and a plate is considered. The inevitable unideal bonding between the actuator and the plate is taken into account through a spring model. The solutions, which can simultaneously satisfy the governing dynamic equations and the boundary conditions, are obtained in a closed form using trigonometric series and modal superposition method. The convergence of the series is demonstrated and the model is numerically validated through comparisons with FEM results using a PZT-5H wafer bonded on the surface of a thin aluminum plate. The proposed model is general, suitable for both ideal and unideal bonding, and expected to provide a useful tool for analyzing and simulating SH0 wave generations in SHM applications.
Crack Initiation and Growth Acoustic Emission Analysis with Finite Element Analysis for Structural Health Monitoring of Mooring Chains
Mooring chains for floating off-shore installations, typically designed for a 25 year service life, are loaded in fatigue in a seawater environment. Moreover, the life of old structures has to be extended. This includes the replacement of expensive periodic in-service inspections with cost-efficient Structural Health Monitoring (SHM) with permanently installed sensors. Although successful at detecting and assessing the fatigue cracks, the hazardous nature of the operation calls for remote techniques that can be applied continuously to identify damage initiation and progress. Appropriate replacement plans must enhance current strategies by implementing real-time data retrofit. Acoustic Emission (AE) technique is capable of detecting cracks and fatigue dam in mooring chains. This paper describes a methodology of Finite Element Analysis (FEA) for crack initiation and crack growth simulation for Structural Health Monitoring (SHM) applying AE technology for damage detection.
Mathematical Modelling and Numerical Simulation of the On-line Structural Health Monitoring System
To design a Structural Health Monitoring (SHM) system, it is important to understand phenomenologically and quantitatively the wave propagation in composite materials and the influence of the geometrical and mechanical properties of the structures. To accelerate the design of SHM systems, the fluid-structure interaction (FSI) effect on the wave propagation has to be considered. In this contribution, a concept of coupling FSI with an ultrasonic wave propagation is proposed, which is referred to as eXtended Fluid-Structure Interaction (eXFSI) problem. The eXFSI is a one-directional coupling of typical FSI problem with an ultrasonic wave propagation in fluid-solid and their interaction (WpFSI). The WpFSI is a strongly coupled problem of acoustic and elastic wave equations and automatically adopts the boundary and initial condition from previous time step. To the best of our knowledge, such a model is new in the literature. The FSI is modelled in terms of the arbitrary Lagrangian Eulerian (ALE) technique and couples the isothermal, incompressible Navier-Stokes equations with nonlinear elastodynamics using the Saint-Venant Kirchhoff (STVK) material model. The WpFSI problems are solved on the moving mesh which is automatically adopted from the FSI problem at each time step. The ALE approach provides a simple, but powerful procedure to couple solid deformations with fluid flows by a monolithic solution algorithm. However, Combining fluid dynamics with structural analysis traditionally poses a formidable challenge for even the most advanced numerical techniques due to the disconnected, domain-specific nature of analysis tools. The principal aim of this research is the exploration and development of concepts for the efficient numerical solution of the eXFSI problem. The finite element method is used for the spatial discretization. Temporal discretization is based on finite differences and is formulated as a one step- scheme, from which we can consider shifted Crank-Nicolson and the fractional-step- schemes. The nonlinear problem is solved by a Newton-like method. Our application of the eXFSI and WpFSI models is the design of on-line and off-line Structural Health Monitoring (SHM) systems for composite material and lightweight structure, respectively. The implementation is accomplished via the software library package DOPELIB.
Mitigation of Adhesive Nonlinearity in Nonlinear-Lamb-wave-based SHM Systems
In practical structural health monitoring systems, the existence of the adhesive nonlinearity may jeopardize the damage diagnosis if the non-damage-related sources are non-negligible. In this work, a system optimization is carried out with the focus on the wave generating process based on a previously established nonlinear shear-lag model, aiming at mitigating the adhesive nonlinearity. The effectiveness of the optimization strategy is validated in both finite element and experimental perspectives. For the former, as the adhesive nonlinearity and the material nonlinearity of plate can be separately introduced to the system, their corresponding nonlinear responses can be directly compared between the unoptimized and the optimized systems. The dominant influence of the material nonlinearity of the plate in the optimized system is highlighted. For the latter, the cumulative effect of the S mode Lamb wave responses is investigated to determine the dominant nonlinear source. Results show that a clear cumulative phenomenon can be observed in the optimized system, which means the adhesive nonlinearity is effectively mitigated
Modeling, Simulation and Analysis of Electromechanical Impedance-based SHM using Finite Element
Electromechanical impedance-based (EMI) damage detection techniques are based on the capability of piezoelectric materials to act as sensors and actuators and they have contributed to the development of structural health monitoring (SHM) systems. Classic techniques use a PZT (Pb-lead Zirconate Titanite) transducer attached to the monitored structure and measure the impedance signature of the couple PZT-Structure. However, EMI based techniques are depending of different factors such as frequency range, number of PZT, ambient temperature, type of structure, among others. Thus, in order to demonstrate the effectiveness of EMI-based methods, it is necessary to carry out practical experiments, which is not a trivial task when such factors have to be considered. Therefore, in this paper, it is presented an EMI-based finite element (FE) model developed using PZFlex® software, which is based on FE. Two different structures were simulated and evaluated: a rectangular aluminum plate and a steel pipe. Simulated results were compared with practical ones and show that the proposed model can be a powerful tool for developing EMI-based SHM techniques.
Modeling, Simulation and Analysis of Temperature Effects on Impedance-based SHM Applications using Finite Elements
Among several different methods for structural health monitoring (SHM), the ones based on electromechanical impedance (EMI) have been received special attention from researchers. EMI based methods use inexpensive piezoelectric transducers, such as PZT (Pblead Zirconate Titanate) patches since they are simple to implement, extremely light, noninvasive, and are able to perform self-actuating and sensing to detect local damage. However, EMI based methods are very sensitive to environmental changes, which affect transducer properties and have hindered its application in real-world structures. The ambient temperature has been cited in the literature as a critical problem for practical EMI based applications. Therefore, in this paper, it is presented an analytical and numerical study to characterize the effect of temperature on PZT transducers applied to SHM. For that, an EMI-based finite element (FE) model was developed using PZFlex® software. Simulated results compared with experimental ones show that the information obtained can be a powerful tool for developing methods to compensate temperature effects on EMI-based SHM systems operating on wide frequency bands. Tests were carried out varying the temperature from - 20°C to + 70°C for a frequency range of 0 – 300 kHz
Measurement and Modelling of Thermal and Mechanical Anisotropy of Parts Additively Manufactured using Fused Deposition Modelling (FDM)
Fused deposition modelling (FDM) is an additive manufacturing (AM) technique which involves melting a thermoplastic filament material and subsequently extruding it, layer by layer, to create three-dimensional objects. The nature of this build process yields parts with inhomogeneous compositions, which may result in anisotropic thermal and mechanical properties. In this work, such anisotropies were investigated for different commercially-available FDM materials such as polylactic acid, acrylonitrile butadiene styrene, and polyurethane. Due to the biaxial symmetry of some properties of resulting FDM parts, a transversely isotropic material model was developed for simulating the FDM part response to thermal and mechanical loads. Such a model is more robust than an isotropic model and, when compared to a full orthotropic model, requires fewer elastic constants to be experimentally determined. Ultimately, the development of FDM-specific thermomechanical property data and models for AM parts will provide more accurate parameters for part designs, leading to higher confidence in part qualification.
Adaptive Prognosis of Fatigue Damage Based on the Combination of Particle Filters and Neural Networks
A method for the prediction of the residual life of a component subject to structural degradation which stems from the combination of a particle filter with an artificial neural network is proposed in this study. The artificial neural network is adaptively trained on-line, i.e., its parameters are identified in real time by the particle filter as new observations of the structural degradation become available from a generic diagnostic system. The adaptive network is then used to perform a multiple-step ahead prediction, thus estimating the probability density function of the residual life. The advantage of the method is that it can potentially adapt to different trends in the damage evolution, even in presence of anomalous behaviors due to failures or unforeseen operating conditions.
Battery Aging and Fuel Efficiency as Optimization Objectives as Part of a Real-time Operating System of a Multi-source HEV
Modeling and effecting battery health is a crucial issue in the electrified transportation field. Beside enormous battery cost, also the competence of modern transportation systems strongly depends on the functionality of batteries. Beside diagnosing the battery state/health, an integration into control-/power management systems is important to effect the battery status. Power management strategies designed for Li-Ion battery powered hybrid electric vehicles (HEVs) should consider battery health as an optimization objective as battery health can be severely affected by charge/discharge patterns. For model-based estimation of battery health, various models have been proposed in the literature. These models can be grouped into two categories: one emphasizing on internal chemical reactions in the battery for example electrochemical models; and another focusing on experimental laws. The aim of this paper is to explicitly show how aging can be modeled and therefore considered within operation and control systems. Here aging-related parameters are integrated in a multi-objective optimization algorithm to generate solutions that satisfy the conflicting objectives of fuel minimization and battery life extension. The rule-based power management developed previously is modified to include batteries state of health (SoH) in designing rules. A dynamic battery model that integrates the effects of temperature and capacity fading is used. Some of the main factors responsible for aging are temperature, time, and cycling. Both number of cycles and depth of cycles play a key role. In this paper the power management strategy is designed to manage the battery health degradation in some optimal sense. First, formulating an optimization problem for maximizing the usable capacity of the battery, second, utilizing a supercapacitor to take over the peaks in demand while limiting the battery current. An aging model and a suitable algorithm is used to analyze the cycling effects of an irregular load profile on remaining battery capacity
Integrated Prognostic Model for RUL Estimation using Threshold Optimization
The capability of prognostic models to estimate loss of machines functionality, enables plant operators to manage the system maintenance and related logistics tasks (spare part management) effectively. Prognostic models will be a key feature and therefore connect the operation of machines and systems in higher modes of automation and factory digitalization, denoted as Industry 4.0 (Germany), Industrial Internet (US), and China 2025 (China) focusing to machines life cycle with respect to reliable and continuous operation and functionality. Prognostic models allow to estimate reliability characteristics mainly related to lifetime and reliability’s characteristic (hazard rate, availability). Using the models during operation, operational cost and in the best case due to connected maintenance strategies unscheduled machine downtimes can be reduced. Through appropriate control strategies, it is possible to preserve the service lifetime based on the information of damage accumulation in unpredicted circumstances. Even with inadequate informations extracted from monitoring data, prognostic schemes allow to predict upcoming physical characteristics that permits a higher level of condition-based system maintenance. The optimization of integrated lifetime model becomes vital for accurate estimation of machine’s state and therefore the Remaining Useful Lifetime (RUL). In this work, a previously developed, novel state-machine model combined with parametric approaches is extended. The approach can be characterized as parameterized state machine model. During training, experimental data including stochastically effects stress-related data are combined with the moment in time of functional loss; related parameters, and thresholds are combined to define a model by optimization. The threshold optimization will identify the best optimal solution. The application (test) for unknown system data demonstrate that these new kind of prognostic model is able to estimate the RUL with high accuracy. In this paper, the optimization of the model containing the structure of state machine as well as related parameters are discussed. Experimental results demonstrate the effectiveness of the improvement.
Multi-parameter Fibre Optic Sensing System for Remote Condition and Operation Monitoring of Gearbox Bearings in Rack and Pinion Jacking Systems
In a world where more than ever “time is moneyâ€, there is a continuous push for new technologies that enable condition and operation monitoring of critical industrial equipment in often inaccessible locations and very harsh environments, thus minimising the need for corrective actions, down-times and at the same time providing better insights about their operation. In this paper, we demonstrate the technical feasibility and the inherent advantages of Fiber Bragg Grating (FBG) based fiber optic systems to remotely sense, in real-time, multiple performance parameters of critical gearbox bearings inside rack and pinion jacking systems. The bearing condition and operation monitoring system jointly developed by Smart Fibres and SKF includes numerous FBG-based optical sensors to monitor temperature and both dynamic and static strain, which were deployed in various points inside the outer race of the sensing bearing. Smart Fibres’ SmartScan 04 FBG interrogator acquired real-time measurements of all the aforementioned parameters, which thanks to SKF’s data analysis expertise and its knowledge of bearings and loads were combined and transformed into meaningful insights about the condition and operation of the gearbox’s bearing and the jacking system as a whole. An innovative method to instrument rolling element bearings was put in practice during this project. For the gearbox bearing instrumentation on this jacking system, two FBG arrays, each containing 15 strain sensors and 1 temperature sensor were fixed in a groove machined on the outer race of the bearing’s structure. This allowed us to measure real-time temperature, rotational speed and dynamic/static loads at multiple points around the bearing’s structure. Not only did this provide a more direct indication of the bearing’s condition than conventional strain gauge based load cells, but it also provided valuable information about the alignment and loading of the bearing both in running as well as in stand-still conditions. Some of the intrinsic characteristics of fiber optic sensors make them particularly suitable for this type of application. Complete immunity to electromagnetic interference enabled our sensors
New Methodology for Testing Flight Actuators in Aiding Loads Catering for Large Prognosis Data Needs
A large variety of actuators are used in aerospace applications. They require test benches for qualifying them for flight operation. These test benches are generally of opposing type, where in the actuators are applied opposing loads and the actuators has to prove their capability working against them. Costly Flight load input profile test benches are needed to simulate the actual flight loads including aiding loads to the flight actuators (load applied in the direction of motion of actuator output shaft). This paper presents an idea of using same actuators for both actuator under test (AT) and loading actuator (AL). Both AT and AL will be connected in tandem and load requirements can be met with controlling the current or supply voltage to AL. The required loading direction can be met by advancing or lagging the command to AL in reference with AT. This has many advantages like inherent load, speed, power and bandwidth matching. As both AT and AL are similar, each test run will result in twice the amount of data making it more useful for data intensive experimental prognosis of such systems. The concept is verified with simulation runs on Simulink model representing actual hardware. Experiments are done on analog rotary actuator and linear digital actuators to prove the concept. Current, Voltage and lag controls are experimentally verified for their applicability
Study on Non-linear Regression Algorithm for Prognostic and Health Management of Aero-engine
Condition-based maintenance (CBM) can be broadly applied to aero-engine prognostic and health management (PHM) in order to economize the cost of repair and to improve the systems efficiency. PHM process of aero-engine usually requires prediction for rest useful life (RUL) and health indicator (HI) as feedback information for CBM. In general, aero-engine PHM algorithm predicts RUL and HI values based on historical aero-engine work record. Former PHM methods which demand all historical data to estimate RUL lead to a credible estimation but at the same time result in increment of cost and inconvenience of PHM process. To provide more flexible and lower requirements for data maintenance PHM approachs, this paper proposes a deep learning (DL) algorithm to predict RUL and HI based on only one flight cycles working record. The designed DL algorithm is named recurrent neural network based feature extraction non-linear regression algorithm (RNNFE-NLRA), which combines a recurrent neural network to extract deep features from working record during one flight, a feedforward neural network to be a non-linear regression for estimating RUL and HI, a Kalman filter to rectify the predicted result. A newly published data set including 35 aero-engine samples is used to verify the applicability of the algorithm proposed. We also evaluate the designed model against other related works. The results show that the designed algorithm estimates an RUL with the same accuracy for different stages of aero-engine degradation process while former PHM approaches estimate with difference. A stable accuracy for all stages of degradation returns more generalization ability. Furthermore, by using only one flight information, our algorithm can reduce the models complexity and economize data maintenance cost and can operate in a more flexible and efficient way than former works. Due to the advantages above, the designed algorithm can not only be used in aero-engine PHM but also in a similar complex engineering systems PHM case.
Wheel Polygonalization Feature and Re-Profiling Evaluation Method and Application based on Vehicle Interior Noise Monitoring
In this paper, a rapid evaluation method of wheel polygonalization feature and reprofiling, which is based on vehicle interior noise monitoring and coherent relationship between noise spectrum and wheel out-of-round wear, is proposed for high-speed train. More than eight hundred high-speed trains, including five vehicle types, were monitored for each coach interior noise, and wheel polygonalization state and re-profiling were evaluated with this method. Furthermore, the influence of vehicle type, vehicle interior noise level, wheel out-of-round wear excitation frequency and order characteristics as well as running mileage after wheel re-profiling on wheel polygonalization were respectively analyzed using statistical analysis of the measured data. The results indicate that the proposed method has strong feasibility in field operation, and excellent monitoring effect of wheel polygonalization state. The results of correlation analysis are important for the vehicle interior vibration and noise control of high-speed trains and the study of wheel polygonalization.
An Investigation of the Performances of Embedded Buckypapers Within a 0/90 WC Glass Fibre Reinforced Composite Specimen with a Delamination, under Tensile Loading
Buckypapers (BP) are made from CNT that arrange to produces a thin layer which are bonded to each other by van der Waals forces. One of the sensitive characteristic of a buckypaper is change of its resistance due to a nano scale change to its geometry. Due to this reason BP has been widely researched for strain measurement of various structures and became a potential candidate for sensing of structural performance under operational loading. Due to compatibility with fibre reinforced composites; BP is becoming an attractive candidate for structural health monitoring (SHM) of composite structures. This paper detailed an investigation conducted on embedded BP strips closer to a purposely created delamination within a glass fibre composite sample under tension loading. In this study, a FBG sensor was also embedded at a location closer to BPs for comparison purpose. The change of electrical resistance of BP under tensile loading was investigated. Interestingly the change of electrical resistance of BPs was following the same trend as FBG sensor strain readings. Further, when the delamination damage approach the locations of BPs, the measured resistance of BP has increased significantly showing strain concentrations around the crack tip.
Conductive Poly Lactic Acid (PLA) Temperature Sensors Embedded in Additively Manufactured Parts
This paper explores the temperature sensitivity of PLA filament doped with carbon black (cPLA) embedded into an additively manufacturing PLA part. The electrically conductive cPLA senses temperature changes within the PLA part without loss of strength in the part or added complications of installing a commercial sensor while printing. Measuring the resistance changes in these conductive segments will provide information about the state of the part based on resistive response to temperature loading. In this case, we plan to determine the relationship between the temperature of the sensor and its resistance. Results suggest that printing parameters and geometry of the conductive sensor will change the magnitude of the relationship; however, a consistent correlation between the two values was demonstrated. With continued research and enhanced print consistency, a proven relationship governing the temperature and resistance of a cPLA gauge could allow valuable insight into temperature behavior and distribution inside of an additively manufactured part.
Development of a Multifunctional Stretchable Sensor Network for Smart Structures
Smart structures revolutionize the traditional manned systems and challenge the structural health monitoring (SHM) field from a completely new perspective. Advanced sensing technology enables highly intelligent systems by embedding sensor networks into composite materials for smart structures such as fly-by-feel flights and intelligent robots. In this paper, we present a highly stretchable network with multifunctional sensor nodes microfabricated on a flexible polymer substrate. For the first time, we are capable of integrating three types of sensors with their appropriate materials into one single network. 6 platinum resistance temperature detectors (RTDs), 27 constantan strain gauges (SGs) and 8 lead zirconate titanate piezoelectric transducers (PZTs) with their unique patterns are distributed over the entire network. Finite element modeling has been performed to analyze the stress distribution along the node-wire connections during the stretching process in order to minimize nodal rotations. By taking advantage of a serpentine wire design, the network can be expanded 700% in area while maintaining high yield and low variation. A series of sensor characterizations have been conducted to validate the functionality of RTDs, SGs and PZTs. It is worth mentioning that the sensitivity of the RTD is 0.1 V/°C and the minimum detectable strain of the SG is as low as 2.26 με. The multifunctional sensor network will be embedded into a composite wing to provide real-time aeroelastic feedbacks in a wind tunnel
Distributed Smart Sensing on the Fly: Dynamic Task Allocation in Wireless Sensor Networks
Structural health monitoring (SHM) applications generally utilise high sampling rates, which the low-power wireless protocols used for mesh networking are not well equipped to handle. This makes the deployment of low-power wireless sensor networks for SHM a challenging problem, as the transmission of raw data is slow, depleting battery life quickly. Smart sensing approaches try to overcome this by processing data at the sensor nodes. Most such approaches are preprogrammed and static. This causes two major issues: First, the application logic cannot be easily modified after deployment. Secondly, there is limited ability to adapt to changes in the environment or degradation of hardware. To address these problems we have developed a smart wireless sensor network system which allow users to specify and submit their sampling and computational logic on the fly in python through their web browser in a MapReduce style syntax. The computational tasks specified by users are modelled by the system as a directed acyclic graph (DAG). For every operator node in this graph we predict the computational work required, and for every edge we predict the volume of data to be transmitted. We also track the computational capabilities and wireless connectivity of every sensor node. Combining these, we formulate and solve an integer linear programming problem to optimally allocate tasks to nodes in the network. Through simulation, we demonstrate how this approach reduces power usage for typical SHM applications, and offers robustness and resilience under varying wireless connectivity and node processor speeds.
Elasto-acoustic Wave Source Localization in Composite Plate-like Structure using Muti-rosetts Sensing
The rapid technical progress in the engineering (applied science/technology) requires more advanced materials. The desire to reduce the weight of structure and reduce their manufacturing costs may affect the safety and reliability of designed structure. The structural damage can occur due to many factors which are difficult to predict in advance, e.g.: sudden impact loads. An external impact can cause an internal damage in the material with no visible marks on the external surfaces of the composite element. The possible hidden damage can be the source of a further mechanical deterioration of the composite structural element. Damage detection in the structures is an active research area since decades. Number of the studies is carried out in order to meet this objective, particularly, the identification of the damage size and location or to find direction of the impact event. Recently on of the promising monitoring technique method is based on fibre optic sensors (e.g. fibre Bragg grating sensors, FBG). It is due to the advantages of FBG sensors such as small size and weight, high corrosion resistance or no calibration requirements. Also it is very easy to multiplex FBG sensors and make rosette array that can be implemented on different kind of structures. The goal of the research is to analyse impact detection capability of different FBG strain rosettes types. Additionally the sensitivity and area of working individual rosette will be considered. The experiment will be carried out on composite plate with the use of impact and/or elasto-acustic wave source. The proposed impact detection method by rosette network can be applied on structures like plane wings, turbine blades or ship masts. The locations of detected impacts can be further analysed by more sophisticated methods (terahertz spectroscopy or infrared thermography).
Embedded Piezoelectric Transducers in Carbon Fibre Composites for Nonlinear Ultrasonic Applications
There has been a growing interest over the last few years in developing “smartâ€ carbon fibre reinforced plastic (CFRP) composites with integrated piezoelectric lead zirconate titanate (PZT) transducers, which are able to provide real-time ultrasonic monitoring of aircraft components. However, piezo-sensors embedded in CFRP laminates require electrical insulation layers due to the electrical conductivity of carbon fibres, which may compromise the mechanical properties of the composite. Typically, non-conductive materials such as polyimide (Kapton) films or polymer coatings are used as interlayers between the sensor and the composite ply for electrical insulation. Nevertheless, these materials increase the level of material distortion around the PZT and can dramatically reduce the adhesion between laminate plies. Thus, the structural integrity of the component may significantly be affected. This paper proposes a novel design of embedded PZTs in CFRP composites, which consists of insulating the conductive surface of the PZT with a thin layer of plain glass fibres. In this research work, the effect of PZT embedment on the flexural and interlaminar shear strength of CFRP laminates was experimentally investigated through long-beam and short-beam three-point bending tests. A failure analysis inspection of the laminates subject to mechanical loading was also conducted using an optical microscope. Test results revealed that the proposed configuration had no impact on the interlaminar properties of the CFRP laminate. In particular, the difference in maximum stress and deflection to failure between the laminates with embedded PZTs and plain laminates was less than 3%. Moreover, a pitch-catch nonlinear vibro-acoustic experiment was performed to study the sensitivity in damage detection of the proposed configuration of embedded PZTs compared to the case of conventional surface-bonded PZTs. The results obtained from CFRP laminates with in-plane delamination showed a higher level of second harmonic generation at the local damage resonance (LDR) frequency using this configuration of embedded PZTs. Besides applications on in-service monitoring of aircraft components, such “smartâ€ composites have the potential to be used within other engineering sectors.
Expandable Sensor Networks for Structural Health Monitoring
Autonomous space habitats that can sense their health, detect damage, and also monitor the environments like a human can revolutionize the space industry. Current Structural Health Monitoring (SHM) systems have demonstrated the ability to detect, locate, and determine the size/severity of structural damage in metal and composite structures. However, there currently are no long duration flexible hybrid multifunctional devices that can conformably distribute sensors over very large surfaces enabling the availability of instantaneous information on the structural integrity of the structure while adding minimal weight. This presentation will showcase recent work conducted to build expandable autonomous sensing capabilities and integrate flexible structural health monitoring into space materials such as stateof- the art thin-ply composites and soft-goods materials for expandable habitat structural concepts, during or after fabrication, to enable evaluation of structural properties and failure prediction over the duration of the habitat’s operational life
Extending the Use of Power Tools to Aerial Robots for Infrastructure Repair
The use of UAVs for aerial inspection of infrastructure has many obvious advantages over manned approaches, including cost and safety. Prior work has included a variety of modalities for non-contact measurement such as basic computer vision, specific use of infrared or microwave wavelengths, and lasers to assess structural integrity. Here we use UAVs in a direct contact fashion for the sake of repair after inspection. The goal of this work is the repair of composite structures, such as wind turbine blades, which requires both precise and high-force manipulation of tools for the material removal (sanding and drilling) that precedes patching. Here we present the design of a customized delta manipulator along with analysis of the implications of linkage length on force production relative to tasks associated with composite structure repair.
Impact and Damage Localization with Integrated Active and Passive Sensing using L-shaped Sensor Arrays
This paper presents a technique for detecting structural impacts and damage by integrating passive and active sensing approaches. An L-shaped sensor array was used to detect and localized impacts by measuring the response of structures. It was found that, because this method does not require prior knowledge of structures such as the wave velocity profiles, accurate results could be achieved even on anisotropic structures. This sensor array scheme was then extended to include an active sensing approach. With the use of an actuator, the same sensor array was used for damage detection by measuring scattering and reflected waves. A series of experiments was carried out to demonstrate the proposed techniques. The superior capability of the proposed techniques was demonstrated
Integrated Hybrid Structural Health Monitoring System
Structural Health Monitoring (SHM) is increasingly being evaluated by the industry as a possible method to improve the safety and reliability of structures, thereby reducing their operational cost. Effective SHM systems must possess the ability to detect and track structural damage, as well as monitor environmental and loading conditions the structure experiences. Two types of information are needed to accurately predict structural integrity: damage data and structural response. Current SHM systems utilize Piezoelectric (PZT) actuators and Fiber Optic (FO) sensors separately. PZT transducers typically detect and track damage while FO sensors typically monitor loads and environmental parameters. A hybrid PZT-FO diagnostic system can capture damage and load data by using PZT actuators to input controlled structural excitation and FO sensors to measure the corresponding structural response. This presentation discusses an integrated systems approach for a hybrid SHM system. The hybrid PZT–FO sensor network consisted of a hybrid sensor network, connectors, and data acquisition hardware/software integrated into a single unit to take advantage commonality in electronic components. The FO and PZT sensors were configured and designed for integration with the structure without structural degradation. This presentation demonstrates the capability of the hybrid system and its ability for damage detection and static/dynamic-load monitoring capabilities.
Multifunctional Skin based on Spiral Passive Electromagnetic Sensor for Efficient Structural Health Monitoring of Composite Structures
The objective of this research work was to create and validate a low-cost smart-sensor for non-destructive evaluation (NDE) and structural health monitoring (SHM) to be easily embedded or surface mounted on complex aerospace composite skin structures. A Spiral Passive Electromagnetic Sensor (SPES) was designed and fabricated for damage detection. The sensor is a planar two-dimensional inductor circuit of scalable size that resonates at a characteristic frequency when exposed to an electromagnetic field. A change in a material's permittivity or permeability due to the presence of flaws within the structure can be sensed with the SPES by monitoring its resonant response. The devices described herein has already demonstrated, in previous works, the ability to detect flaws within conductive (i.e. carbon fibre) and non-conductive (i.e. fibre glass) structures[1, 2], [3, 4]. Moreover, the SPES can act as de-icing device and detect temperature and humidity variation [5, 6]. A study was conducted to investigate the capability of sensor to give information on the location and on the size of delamination. Multiple sensors were embedded in a smart-layer together with the antenna to create a sensing skin able to monitor composite structures. The sensor use a passive wireless resonant telemetry scheme and/or a wired interrogation method. The use of a passive system eliminates the need for on-board power and exposed interconnects, reduce the instrumentation mass and volume, increasing the life of the device and the reliability due to the continuous operation even in case the damage affect the sensor itself. The sensor presents a great potential to monitor defects in composite structure, acting as SHM sensor. The technique illustrated is able to locate and quantify the presence of damage, giving information on the defect before it causes a failure of the structure.
Development of Nanocomposites Quantum Resistive Sensors (QRS) for the Structural Health Monitoring (SHM) of Composite for Wind Turbine Applications
The growing development of renewable energies is leading to the fabrication of larger composite parts that will be subjected to more severe environment. The optimization of these systems health monitoring will be compulsory to estimate their damages and reduce the maintenance costs. The development of nanocomposites Quantum Resistive Sensors (QRS) for the monitoring of strain and damage of composite structures is presented. QRS are made with the addition of carbon nanotubes in an epoxy resin. Those sensors can be on the surface or embedded in the core of the material during its fabrication. Thanks to their low thickness (1-2 μm), they can provide in situ monitoring without the degradation of mechanical properties, and the creation of new defects. The characteristics of these sensors can be adjusted by the fraction of CNT in the resin or the amount of resin and CNT in the sensors. The electrical response of the sensor due to a mechanical solicitation is explained.
Optimisation of Optical Fibres for Structural Health Monitoring Through Micro-braiding
Structural health monitoring (SHM) of composite structures has become paramount in aerospace industry in recent years. With advances in research and innovation, this can be achieved in real time by embedding optical fibre into the structure to be monitored. The optical fibre has been proven to be versatile for application in SHM due to its ability to measure strain and temperature. However, the optical fibres are brittle which makes their handling and integration difficult. This issue can be solved by micro-braiding the optical fibre, with the judicious type of reinforced fibre. Braiding is a textile forming (both tubular and flat) technique that is capable of producing near net shaped structures, in which the tow such as glass fibre can be interlaced at bias angle (braid angle). This paper presents the optimised material selection, fibre orientation and machine speed. Variation in braid angle is achieved by varying the take up speed while keeping the braider machine speed constant. The tensile properties of the conventional and micro-braided optical fibre are presented and compared. The improvement of tensile strength and strain is observed and discussed.
Wireless Thin Film Strain Sensing Circuit for Implantable Applications
Recent advances in osseointegrated prosthetics highlight the need for monitoring physical phenomena within the human body. Wireless, in vivo strain sensing could be an impactful way to address the practical challenges faced by medical professionals in determining the health and healing state of prosthetics in the body. A biocompatible, wireless strain sensor is presented here based on a thin film inductor-capacitor circuit fabricated with gold and polyimide. Strain sensitive capacitors are designed and tested to confirm functionality as sensing elements. Inductors are designed for wireless readout and tested to confirm that a careful design avoided a strain response in the inductor. The components are integrated in a thin film sensor assembly with a simple but effective process flow. The wireless strain sensors are fabricated and strain tested in a laboratory environment. This provides the basis for addressing additional challenges of in vivo sensing using the described design and exploring similar thin film approaches to sensing biological phenomena.
Wireless Torque Sensing System for Rotating Machinery
There is a growing need for advanced, cost effective, highly reliable, durable and non-intrusive systems for Structural Health Monitoring (SHM) and Condition Based Maintenance (CBM). Of particular importance is the Structural Health Monitoring of rotating machinery (motors, generators, pumps, gear systems, etc.). Such systems should be able to detect failures in their early stages so that the repairs would be less expensive or, even better, it should be able to predict the critical conditions so that preventive actions can be taken to avoid possible damage. Determining stress in rotating machinery is a complex task and surface acoustic wave (SAW) devices provide a unique opportunity to wirelessly sense torque. In this paper, we present the results of design, fabrication, and characterization of torque sensors based on SAW technology. SAW based passive torque sensors were developed and built on quartz piezoelectric substrate using current IC photolithography techniques. A SAW sensor interrogator/reader and the associated software were developed to measure torque. The torque sensing system was tested both statically and dynamically on a specially designed test rig. Due to its small thickness and low weight, Albido’s sensors can be used on moving structures with minimum loading and without disturbing the aerodynamic properties. The proposed system is low cost, low maintenance, reconfigurable, adaptive, scalable, easy to install on new or existing structures, and does not disrupt the environment.
Analysis and Optimization of Vehicle-borne Gateway Based on Finite Element Technology
The vehicle-borne gateway, as the base of intelligent transportation, is affected by road surface impact or other uncertain factors during the driving process. The mechanical dynamics of the structure directly affect the electrical signal transmission and system reliability. Hence, excellent anti-vibration and anti-shock performance are critical issues that should be considered in the design stage of the vehicle-borne gateway. In this paper, a comprehensive and effective finite element analysis method is presented to predict possible chassis vibration due to external incentives and analyze the vibration transmission path. This method is applied to the finite element simulation of a certain type of gateway, and the accuracy of the simulation is verified by modal test. Furthermore, as a key component of gateway information storage, the dynamic performance of hard disk component is considerable and should be focused especially. In order to reduce the impact of vibration on the hard disk, this paper performs structural optimization and proposes vibration reduction measures. The effectiveness of the optimization method is verified by the finite element simulation. The simulation results also provide the foundation for life prediction of gateway system.
BIM-based Design of Structural Health Monitoring Systems
Although structural health monitoring (SHM) and control is continuously gaining importance, information about SHM and control systems, referred to as “monitoringrelated informationâ€, cannot be fully described on a well-defined, mathematical basis. Monitoring-related information, such as information about the embedded algorithms of sensor nodes deployed, about communication protocols, or about the overall SHM strategies implemented, can advantageously be used for SHM and control system optimization or for the documentation of the SHM and control. The study reported in this paper enables monitoring-related information to be described using building information modeling (BIM). The BIM-based approach builds upon the Industry Foundation Classes (IFC), an open BIM standard facilitating the interoperability of BIM models. As will be shown in this paper, the standardized IFC schema will be extended, facilitating BIM-based modeling of SHM and control systems in compliance with the IFC standard. Using the extended IFC schema, a prototype SHM and control system is designed, implemented, and validated on a laboratory test structure.
Digital Structural Health Monitoring for SHM Design
Structural Health Monitoring (SHM) technology is gaining more and more acceptance in world-wide industries as a new innovative approach for providing information regarding the state of health of structural platforms in real-time or ondemand through distributed sensor networks built onto the structures. The main challenge, however, associated with SHM is that sensors used in the process are permanently mounted on the structure; therefore, it is desirable to have the optimal sensor location to provide the highest SHM performance before deploying the SHM system for real time monitoring of the structures. Digital Structural health monitoring (DGSHM) is a new concept which utilizes simulations instead of experimentation to characterize the performance of SHM systems with respect to various parameters that may affect the results of the SHM predictions. Therefore, the implementation of the concept requires the involvement of three components: 1) A physical model that can produce sensor signals from simulating structural responses, 2) An SHM diagnostic system that can take simulated data and predict the performance of the SHM system, and 3) A sensitivity analysis that would optimize the performance of the proposed SHM system. In this study, an acousto-ultrasound based SHM system is considered. The effectiveness of the proposed framework is experimentally assessed via its application to an aluminum coupon, with three distinct sensor network configurations. The diagnostics results for all three cases are extracted and are used to quantify the sensitivity of the sensor network in terms of prediction error.
Digital Thread offers the opportunity to use information generated across the product lifecycle to design the next generation of products. In this paper, we introduce a mathematical methodology that establishes the data-driven design and decision problem associated with Digital Thread. Our objectives are twofold: 1) Provide a mathematical definition of Digital Thread in the context of conceptual and preliminary design and establish a methodology for how information along the Digital Thread enters into the design problem as well how design decisions affect the Digital Thread. 2) Develop a data-driven design method that incorporates data from different sources from across the product life cycle. We illustrate aspects of our methodology through an example design of a structural fiber-steered composite component.
Strain Sensors Optimal Placement for Vibration-based Structural Health Monitoring
The optimal sensor placement of engineering structures problem with an emphasis on in-plane dynamic strain measurements and to the direction of modal identification as well as vibration-based damage detection for structural health monitoring purposes is revisited in this paper. The approach utilized is based on the maximization of a norm of the Fisher Information Matrix built with numerically obtained mode shapes of the structure and at the same time prohibit the sensorization of neighbor degrees of freedom as well as those carrying similar information, in order to obtain a satisfactory coverage. A new convergence criterion of the Fisher Information Matrix (FIM) norm is proposed in order to deal with the issue of choosing an appropriate sensor redundancy threshold, a concept recently introduced but not further investigated concerning its choice. The numerical model of a composite sandwich panel serves as a representative aerospace structure upon which our investigations are based
Test Event Weighting Coefficients for a Machine Structure from Customer-sampled D-Optimally Placed Wireless Fatigue Nodes
Wireless fatigue nodes (WFN) were used to create a mapping between proving ground test severity and observed 90th percentile customer application severity. This mapping takes the form of coefficients that are associated with each event in the test schedule. The locations of the wireless nodes were influenced by a D-Optimal row selection algorithm that sought to minimize the variance in the calculated test event coefficients. These D-Optimal sensor location selections were based upon simulated machine loads associated with the various events being passed through finite element models, resulting in calculated strain response and fatigue damage rates. After the WFN locations were chosen, the events were all executed experimentally in order to verify that the physically observed damage rate basis matrix would be well conditioned for the purposes of test event coefficient calculation. Finally, random customer-sampled damage rates from WFN were used to estimate the 90th percentile customer damage rate for each node location followed by a least-squares estimate of the test event coefficients. Future designs for the machine can be validated by conducting and/or simulating the selected test events and weighting their damage rates by the calculated coefficients in order to obtain a final 90th percentile customer severity composite fatigue life.
A Method based on Pseudo Excitation Method to Analyze Random Dynamic Stress of Railway Vehicle System
Random dynamic stress of a railway vehicle caused by track irregularity need to be calculated accurately in order to predict the railway vehicle’s fatigue life. It is usually described as the function of mileage. Since the traditional analysis method of this issue is complicated and very time consuming, in this paper, an efficient approach based on Pseudo Excitation Method (PEM) and Finite Element Model (FEM) is developed. By using the proposed approach, the stress power spectral density (PSD) of the railway vehicle contains some specific structures can be calculated conveniently, the related statistical parameters of the coupled vehicletrack system can be obtained accurately as well. The reliability of the proposed approach is verified by comparing with a field test, in which the stress of the front and the rear traction beam of a metro car body is measured. It was proved that the simulation results of the proposed method are in good agreement with the test results, both on the value of magnitude and distribution trend, the relative error of the stress Root Mean Square (RMS) is less than 20% in general, mostly within 10%.
A New Strategy for Damage Identification in SHM Systems by Exploring Kappa Coefficient
Recently, considerable research works have been conducted towards finding fast and accurate pattern classifiers for identifying structural damage when applied to Structural Health Monitoring (SHM) systems. In this way, this paper presents a novel approach for damage identification in SHM systems by proposing the use of the Kappa coefficient as a metric for damage detection in SHM systems. Here, Kappa is also proposed along with PSO-Fuzzy ARTMAP neural network aiming to reduce the amount of attributes, leaving only the most representative features. It is important to highlight that Kappa coefficient analyses the whole confusion matrix instead of using only the principal diagonal as used, for instance, when we compute success rates. Additionally, the Particle Swarm Optimization (PSO) algorithm is used in searching for a set of criteria employees in training damage classifier, in order to maximize the accuracy rate of the classifier. Hence, the Fuzzy ARTMAP Network (FAN) algorithm is used to identify structural damage. The performance of this new strategy is evaluated considering an experimental setup based on the Electromechanical Impedance (EMI) technique, in the time domain, which uses four piezoelectric patches glued onto a unidirectional composite plate. Damage scenarios were simulated by loosening bolts at different positions. The paper discusses the effectiveness of the proposed methodology in light of the experimental results.
Deformation Data Recovery Based on Compressed Sensing in Bridge Structural Health Monitoring
Full life-cycle and real-time structural health monitoring (SHM) rely on numerous, heterogeneous sets of data collected from sensors and extracting feathers that support the estimation of the health condition of a bridge. It is the principal challenges facing the SHM application on transmission and storing such huge amounts of data. Compressive sensing (CS) is a novel data acquisition method whereby the compression is done in a sensor simultaneously with the sampling. In this work, we established the possibility of compressed sensing to address this challenges on bridge SHM. Based on the sparsity of the deformation data of bridge, we proposed a random sampling scheme based on CS to minimize the number of field data. The CS recovery performance is mostly determined by the decomposition basis which is associated with the sparsity of the sampling signal. Different bases have been tested to recover the deformation data. Experimental results demonstrate the proposed method allows a reduction of the measurement data with an acceptable recovery accuracy. And reconstruction performance based on DWT to sparse transform is better than that based on DCT to sparse transform. When the compression ratio is above 0.6, the reconstruction error grows moderately; whereas the reconstruction error grows rapidly as the compression ratio is below 0.6. With the compression ratio decreased from 0.6 to 0.2, reconstruction error is reduced about 2.5 times by using DWT and reduced about 2 times by using DCT.
Eddy Current Imaging for Detecting Defects in Conductive Structures Based on Compressive Sensing Method
Accurate detection and quantitative evaluation of defects and damages in conductive structures is an essential and crucial task in a range of technological applications, such as maintaining the integrity, enhancing the safety, and assuring the reliability of structures. There is scope for improving the current methods by incorporating compressive sensing in the inspection process. In conductive structures eddy current imaging, scientific key problems are sparse representations and transform domain mapping, sparse testing constraints, and sparse image reconstruction. The main research contents are as follows: firstly, constructing the model of conductive structures eddy current imaging and researching on sparse representations, transform domain mapping, sparse matrix, sparse transform matrix, and signal recovery priori errors, for providing basic theory; secondly, using a coil probe to do C scan, and researching on the mapping relationship between the specimen and sparse data acquisition, and the methods of data acquisition, for establishing the theoretical framework of data acquisition; thirdly, proposing the information recovery methods for completing the compressive sensing; fourthly, establishing the experimental system for validating theories and methods. Finally, currently urgent need theories and methods of conductive structures eddy current imaging are established. It will promote the theories and technologies development of eddy current testing. The study and application of conductive structures are also accelerated.
Glass Fibre Reinforced Composite Samples Inspected using THz Spectroscopy
Glass fibre reinforced composites (GFRP) are recently very popular in many industrial branches like: marine, wind energy or civil engineering. From the Structural Health Monitoring (SHM) point of view it is very important to know behaviour of elements during their exploitation. One of recently investigated monitoring techniques are methods based on fibre optic sensors (e.g. fibre Bragg grating sensors) embedded into composite elements during their manufacturing process or into adhesive layer in composite joints. It is possible due to fibre optic advantages like: small size, weight and high corrosion resistance. The sensors can be used for monitoring both internal strain due to mechanical loading as well as strain induced e.g. by moisture influence. One of the problems is that embedding results in local discontinuities due to fibre optic occurrence that can leads to a damage origin. One of the non-destructive methods allow to observe and evaluate the internal structure of non-conductive materials is terahertz time-domain spectroscopy (THz-TDS). The technique can be used for detection and localisation of different damage types like voids, delaminations or mechanical damage. It can be also used for determination of the arrangement of fibre optics embedded into material. The THz method allows detection and localisation of material structural disintegration which occurrence influences on absorption or refractive index of examined material. The paper presents an application of THz spectroscopy for observation and evaluation of the internal structure of thin composite samples with embedded fibre Bragg grating (FBG) sensors and with inclusions (e.g. water intrusion, dust) that were introduced during their manufacturing process as well as a complex composite sample with honeycomb filler.
Identifying Unstable Damage Growth in CFRP Composites under Tension
This paper examines acoustic emission signals from composite specimens under tensile load. Specimens with and without prior impact damage were examined. Traditional acoustic emission parameters do not provide reliable indications of impending failure in these specimens. A dense array of sensor with frequency response extending over 2 MHz was used to monitor acoustic emission signals in this study. The onset of unstable damage in composite specimens was found to the be precipitated by the formation of groups of adjacent fiber breaks, termed multiplets, from microscopic and computed tomography examinations by researchers. The appearance of groups of near identical acoustic emission waveforms close to the final failure may be related to the formation of such multiplets. The size of such clusters of waveforms and their numbers when plotted as a function of load, appears to indicate the impending failure in both undamaged specimens as well as those specimens that were subjected to impact damage.
Method for Extracting Fault Features from Shaft Vibration Signals of Large Turbine Units Based on Gabor Transform Time-frequency Filtering
A method based on Gabor transform time-frequency filtering is proposed to extract special abnormal frequency components from measured shaft vibration signals for stability analysis and fault identification of shaft systems. The abnormal vibration phenomenon appeared on large steam turbine-generator units is analyzed using vibration signals continuously sampled during the process of speed up or speed down. The signals are transformed into time-frequency domain by means of Gabor transform, and abnormal time-frequency components are filtered out using tracking threshold and then the filtered signal is reconstructed so that the fault feature is clearly enhanced. Abnormal vibration response characteristics are investigated and signal features are extracted. The effectiveness of the method is proved by a practical abnormal vibration case in large steam turbine-generator unit
Novel Approaches for Loose Bolt Detection With and Without Sensors using Heterodyning Effect
Detection of defects in the early stages of growth and nucleation is very important in structural health monitoring (SHM) applications. Defects, such as delamination, debonding, loose bolts and cracks create nonlinear behavior at the surface response. Different techniques have been developed to identify the transformation of a structure from the linear to the nonlinear state in presence of defect(s). Nonlinear wave modulation spectroscopy (NWMS) based methods were developed to detect the nonlinearity created due to combination of low and high excitation frequencies. The excitation signal of the NWMS method is created by modulating the ultrasonic probe signal with a low frequency vibration. In this study, SHM and sensor-less SHM (SSHM) methods are proposed for detection of loose bolts. The proposed methods rely on the heterodyne effect. The frequencies of the two harmonic excitation signals are not limited to the combination of low and high frequencies. In the presence of loose bolts the structure behaves like a nonlinear system and new frequencies are generated. The new frequencies can be observed at the summation, subtraction, and other harmonics of the excitation frequencies. The new frequencies may be monitored with sensors, similar to conventional SHM systems. It is also possible to select the difference of the excitation frequencies to be lower than 20 kHz (audible frequency range) to create audible alarms. The new SSHM eliminates the need for the sensor(s) and processor(s). The SSHM may be used to make very low cost SHM systems or it can be used as back up when the sensor(s) or the processor(s) of the SHM system fail.
Nuclear Power Plant PCCV Structure Monitoring Based on BOTDA and Its Data Processing using Kalman Filter
Because prestressed concrete containment vessel (PCCV) is the last barrier of nuclear power plant (NPP), Brillouin Optical Time Domain Analysis (BOTDA) was used in monitoring PCCV structure. However, the layout of distributed optic fiber sensors was not ideal so that the elongation of optic fiber sensors wasn’t uniform. If the monitoring data of a certain time was chosen as a standard, the result of strain variation would have a lot of disturbance points. Facing this problem, Kalman filter was used in data processing. Through Kalman filter, the results were smoother and the disturbance points were eliminated so that the manual work was decreased. As a result, Kalman filter is very useful in BOTDA data processing in the PCCV monitoring.
Robust Hammering Echo Analysis for Concrete Assessment with Transfer Learning
This paper endeavors to develop robust echo pattern analysis system to discern defect-induced anomalous echoes, which can adapt to changing parameters in impactecho test, such as the echo spectrum variation induced by the change in the target concrete types. Conventional computerized impact-echo analysis systems are built on the assumption that the (pre-collect) training echo signal and (unseen) testing echo data reside in the same feature space with identical distribution characteristic. However, in the real-world scenarios, such assumption does not hold. That is, the echo signal is often affected by practical factors, such as selection of instruments, i.e. hammer and transducers, and the change in testing specimen. From the viewpoint of pattern recognition, these factors make the posterior distribution of the test hammering echo signal drift from that of the pre-collected training echoes (with normal/defective conditions), thus will degrade the accuracy of the echo pattern analysis models. In this study, we introduce transfer learning strategy to deal with distribution drift issue between training and input echo signal. For instance, proposed system can distil discriminant echo patterns collected from (training) normal concrete and then generalize the information to detect flaws in lightweight concrete. To validate those schemes, we prepared two kinds of air-coupled transducers and three types of concretes (normal/enhanced/lightweight) with various types of inner void defects. Condition assessment was conducted under mismatched training/testing settings. Extensive experimental results reveal that transfer learning approach can greatly improve robustness of impact-echo.
Time Reversal Invariance for Nonlinear Crack Characterization
The time reversibility of a plane wave packet interacting with a closed crack subject to non-dissipative contact nonlinearity is investigated numerically through a finite element model. It is shown that time reversibility holds for this type contact acoustic nonlinearity, provided that the additional frequency information generated during the forward propagation, such as higher harmonics, sub-harmonics and zerofrequency component, is fully included during the retro-propagation. Alternatively, a loss of information due to a frequency filtering leads to a breakdown of the time reversal invariance. Therefore, a damage index based on the degradation of the time reversal invariance using filtered back-propagation is proposed to evaluate the nonlinear response generated by a closed crack as an alternative to higher or subharmonics measurement
A Data Mining Tool for Detecting and Predicting Abnormal Behavior of Railway Tunnels
The UK railway network is subjected to an electrification process that aims to electrify most of the network by 2020. This upgrade will improve the capacity, reliability and efficiency of the transportation system by providing cleaner, quicker and more comfortable trains. During this process, railway infrastructures, such as tunnels, require to be adapted in order to provide the necessary clearance for the overhead line equipment, and consequently, a rigorous real-time health monitoring programme is needed to assure safety of workforce. Large amounts of data are generated by the realtime monitoring system, and automated data mining tools are then required to process this data accurately and quickly. Particularly, if an unexpected behaviour of the tunnel is identified, decision makers need to know: i) activities at the worksite at the time of movement occurring; ii) the predicted behaviour of the tunnel in the next few hours. In this paper, we propose a data mining method which is able to automatically analyse the database of the real-time recorded displacements of the tunnel by detecting the unexpected tunnel behaviour. The proposed tool, first of all, relies on a step of data pre-processing, which is used to remove the measurement noise, followed by a feature definition and selection process, which aims to identify the unexpected critical behaviours of the tunnel. The most critical behaviours are then analysed by developing a change-point detection method, which detects precisely when the tunnel started to deviate from the predicted safe behaviour. Finally, an Artificial Neural Network (ANN) method is used to predict the future displacements of the tunnel by providing fast information to decision makers that can optimize the working schedule accordingly
A Generalized Approach to Integrate Machine Learning, Finite Element Modeling and Monitoring Data for Bridges
In the last decades, the structural health monitoring (SHM) of civil structures has been performed arguably based on two approaches: model- and data-based. The former approach tries to identify damage by relating the measured data from the structure to the prediction of physics-based numerical models tailored for the same structure. The latter one is a data-driven modeling approach, where measured data from a given state condition is compared to the baseline condition. The data-based approach has been rooted in the machine learning field, where machine learning algorithms are essential to learn the structural behavior from the past data, and to perform pattern recognition for damage identification. In the SHM field, this approach has been known as the statistical pattern recognition paradigm. Basically, in both approaches, the identification of damage requires data comparison between two state conditions, the baseline and a damaged condition; thus in a general sense, those two approaches make use of pattern recognition techniques. This paper intends to step forward through the combination of machine learning, finite element modeling and monitoring data from the Z-24 Bridge in one unique damage detection approach. To achieve this combination, data from simulated undamaged and damaged scenarios can be introduced into the learning process using predictions from finite element models.
A Method of Crack Detection Based on Convolutional Neural Networks
Crack is one of important features in gradually degeneration process for structures, and the cracks of structure can to some extent reflect the health status of the structure. Moreover, convolutional neural networks (CNNs) provide an ideal route to detect cracks utilizing the explosion of Internet and the improvement of computer hardware. In this paper, a method for crack detection based on CNNs is proposed. Firstly, to provide datasets for CNN model training, a large number of crack images ware collected, cut, and labeled. Secondly, a CNN model for crack detection was established through the fine-tuning AlexNet model. Thirdly, the CNN model was trained by the crack images datasets. The trained CNN model is used as a classifier and the crack detection was implemented using the CNN classifier and a sliding window technique. Finally, a crowdsourcing to collect crack images and detect crack is proposed. The objectives of this work are to set up a crack images database for more researchers and obtain a more and more accurate and trained classifier for crack detection via CNNs, and mobilizing the public to collect crack images and detect crack using the trained CNN classifier through the crowdsourcing.
Deep Neural Controller: a Neural Network for Model-free Predictive Control and its Application to Viscosity Control in Chemical Process
This paper suggests Deep Neural Controller (DNC), a network architecture for sequential decision making based on high order Markovian state-space model. DNC is composed of two components, one for modeling system dynamics and another for constructing decision making policy. In this architecture, deriving control policy is conducted by training DNC network. We first employ a deep neural network to model the dynamic behavior of a complex dynamic system that has high-order Markovian property. By integrating the complex neural state-space model with controller network, we can solve high-order Markovian, non-convex control problem with neural network. As a particular example, we employ the suggested method that controls viscosity level in a chemical process
Imputing Missing Strain Monitoring Data in Structural Health Monitoring
Data missing often occurs in structural health monitoring, this article deals with imputing missing strain monitoring data. Raw data collected by strain sensors are divided into stochastic response and temperature-induced response. For stochastic response, the copula is used to construct the conditional distribution of data from the intermittent fault sensor given measurements of a covariate sensor, missing data are imputed by post-processed samples generated from the conditional distribution. To overcome the drawback of conventional copula-based imputation method with fixed marginal distributions estimated from non-missing data, the distribution to distribution regression method is applied to impute missing marginal distributions adaptively. For temperature-induced response, missing data are imputed by the functional regression method. A case study based on field test data from a real bridge is conducted to illustrate the proposed approach.
Reliability of Resonant Frequency Test for Strength Estimation of Concrete using Probabilistic Methods
Nondestructive tests (NDT) are often used to estimate the strength of concrete specimens from empirical models. These models are derived using data from specimens in laboratory controlled conditions or using core samples extracted from structures in service. The performance of the models is evaluated using statistical parameters such as the coefficient of determination and the prediction intervals for a given significance level. Properly fitted model are unbiased and account for both the epistemic and aleatory uncertainties. In this article, probabilistic methods are employed to assess the uncertainties in the strength estimation of concrete specimens using measurements from resonant frequency tests. Probabilistic methods are also used to adjust quality control procedures for the production of concrete masonry units (CMU) when NDT are used to complement data from destructive tests. The adjustments are implemented by increasing the number of samples tested to match results from destructive tests in terms of the probability of detection of a lot not conforming to design specifications and the probability of falsely rejecting a lot meeting design specifications.
Stochastic Subspace-based Damage Detection with Uncertainty in the Reference Null Space
This paper deals with uncertainty considerations in damage diagnosis using the stochastic subspace-based damage detection technique. With this method, a model is estimated from data in a (healthy) reference state and confronted to measurement data from the possibly damaged state in a hypothesis test. Previously, only the uncertainty related to the measurement data was considered in this test, whereas the uncertainty in the estimation of the reference model has not been considered. We derive a new test framework, which takes into account both the uncertainties in the estimation of the reference model as well as the uncertainties related to the measurement data. Perturbation theory is applied to obtain the relevant covariances. In a numerical study the effect of the new computation is shown, when the reference model is estimated with different accuracies, and the performance of the hypothesis tests is evaluated for small damages. Using the derived covariance scheme increases the probability of detection when the reference model estimate is subject to high uncertainty, leading to a more reliable test.
Transfer Matrices-based Output-only Statistical Damage Localization and Quantification of a Frame Structure
Damage localization in civil or mechanical structures is a subject of active development and research. In this paper, vibration measurements and a finite element model are used to locate loss of stiffness in a steel frame structure at the University of British Columbia. Damage localization is not very well developed so far and few methods show promising properties in practice. Among them, the Stochastic Dynamic Damage Locating Vector (SDDLV) approach has interesting capabilities. In this paper, it is compared to a sensitivity based approach developed by the authors. Both approaches have in common to be built on the estimated transfer matrix difference between reference and damaged states. Both methods are tested for localization and quantification on the Yellow Frame structure at the University of British Columbia
A Modal-analysis-based NODIS Method for Beam-like Structure and its Performance under Changing Boundary Conditions
Most widely studied Structural Health Monitoring (SHM) methods need baseline data from the undamaged state and a high number of sensors attached to the structure. In addition, environmental and operating conditions (EOC) can cause false alarms to the SHM system. Among all the EOCs, changing boundary conditions have a great influence on the signals. Thus, these methods are difficult to be applied in real structures for online monitoring. The aim of this study is to determine an efficient damage indicator based on the vibration information of the structure and evaluate the influence of boundary conditions on this method. Based on the structural mode shapes, the NOde DISplacement (NODIS) method which inspects the nodes or stationary points in the displacement mode shape is proposed in this paper. The advantages of this method are: (1) baseline-free, in terms of the comparison with a reference signal is not needed, (2) only a relatively small number of sensors are required. However, the performance of NODIS method under changing boundary conditions needs to be studied intensively. In the present paper, an experiment is conducted on a cantilever beam to investigate the influence of the boundary condition on NODIS method. A Transfer-Function-based Damage Index (TFDI) and wavelet analysis are adopted to investigate the characteristics of the influence
Comparison of Three Time-domain Methods for Damage Tracking of Nonlinear Hysteretic Vibration Isolation System with Unknown Inputs
Recently, vibration isolation technology has been widely applied in different types of structures, which refer to as nonlinear hysteretic vibration isolation systems, to withstand the strong vibration that may damage the structures, influent the structural normal operation, or reduce the life of structures. However, parameter identification and damage tracking of nonlinear hysteretic vibration isolation systems is quite challenging because the restoring behavior of these systems are inelastic. Besides, the external inputs of these systems may not be accurately measured or even may not be measurable in practical applications. To resolve the problems above, in this research, three timedomain analysis methods, which refer to as adaptive extended kalman filter with unknown inputs (AEKF-UI), adaptive sequential nonlinear least-square estimation with unknown inputs (ASNLSE-UI) and adaptive quadratic sum-square error with unknown inputs (AQSSE-UI), have been newly developed to online identify the structural parameters and to real-time track the structural damages. Further, experiment research was conduct through a series of laboratory tests using a scaled vibration-isolation structure model with two typical excitations in the case of different damage scenarios. Finally, the capability of AEKF-UI, ASNLSE-UI and AQSSE-UI methods in parameter identification and damage tracking have been demonstrated and compared in terms of accuracy, convergence and efficiency.
Controlled Substructure Identification of Shear Structure with Virtual Control System
Substructure identification is a powerful tool to identify the parameters of a complex structure. Previously, the authors have developed an inductive substructure identification method for shear structures. However, it was found that the identification accuracy of this method is significantly influenced by certain characteristics of structural responses and, thus, it may not be able to always provide accurate estimation results. To solve this problem, a self-balanced virtual control system (VCS) is introduced into the structure, converting the original substructure identification to a controlled substructure identification problem. By optimally tuning the parameters of the VCS, the dynamic characteristics of the controlled structure can be changed and, thus, the goal of improving substructure identification accuracy is achieved. Finally, a numerical example of 6-story shear structure is utilized to effectiveness of the VCS based controlled substructure identification method
Damage Detection for Beam-like Structures Based on Local Flexibility Method with Rotary DOF Measurement
Many vibration-based global damage detection methods attempt to extract modal parameters from vibration signals as the main structural features to detect damage. The local flexibility method is one promising method that requires only the first few fundamental modes to detect not only the location but also the extent of damage. Generally, the mode shapes in the lateral degree of freedom are extracted from lateral vibration signals and then used to detect damage for a beam structure. In this study, a new approach which employs the mode shapes in the rotary degree of freedom obtained from the macro-strain vibration signals to detect damage of a beam structure is proposed. In order to facilitate the application of mode shapes in the rotary degree of freedom for beam structures, the local flexibility method is modified and utilized. The proposed rotary approach is verified by numerical and experimental studies of simply supported beams. The results illustrate potential feasibility of the proposed new idea. Compared to the method that uses lateral measurements, the proposed rotary approach seems more robust to noise in the numerical cases considered. The sensor configuration could also be more flexible and customized for a beam structure. Primarily, the proposed approach seems more sensitive to damage when the damage is close to the supports of simply supported beams. The proposed approach also shows its potential application to damage detection of wind turbine blades and plate structures.
Damage Identification of Plates by using a Continuously Scanning Laser Doppler Vibrometer System
A damage identification method for plates by using a continuously scanning laser Doppler vibrometer (CSLDV) system is presented. A new uniform-speed scan algorithm is proposed to create a two-dimensional scan trajectory and automatically scan a whole plate surface. Based on the new scan algorithm, the demodulation method is extended from one dimension for beams to two dimensions for plates to obtain a full-field operating deflection shape (ODS) of the plate from velocity response. The full-field ODS of an associated undamaged plate is obtained by using polynomials to fit the corresponding full-field ODS from the demodulation method. A curvature damage index (CDI) using differences between curvatures of ODSs associated with ODSs that are obtained by the demodulation method and the polynomial fit is proposed to identify damage. An experiment of an aluminum plate with damage in the form of 10.5% thickness reduction in a damage area of 0.86% of the whole plate scan surface area is conducted to investigate the proposed method. The damage can be successfully identified near an area with high values of a CDI
Damage Identification of RC Beams from Changes in their Vibration Characteristics
The work presented here investigates the possibility of using changes in the nonlinear vibration characteristics to detect damage in reinforced concrete members. The vibration characteristics were studied by conducting ambient excitation vibration tests on reinforced concrete beams. The damages were detected by examining the changes in the fundamental frequency of the RC beams found from recorded time series data. The tests demonstrated that there is a change in nonlinear vibration behavior with damage. After that frequency domain data of time series were further studied to know the extent of cracks in RC beams due to the loading and a change in nonlinear vibration behavior was observed with increasing damage. The change in frequency content and the area of normalized Fourier Spectrum is prominent for flexure control beams. For flexure control beams area of normalized Fourier Spectrum increased up to the theoretical capacity of the beam and decreased gradually to the state where it could not take any load. However, In case of shear control beams the propagation of crack cannot be identified from the change in frequency content and the area of normalized Fourier spectrum. For both Flexure and Shear control beams, with propagation of crack below a certain sensor showed a jump in normalized Fourier Spectrum plot of that sensor data. These results indicate that the method is useful in detecting high levels of damage in reinforced concrete beams.
Data Fusion based EKF-UI for Real-time Simultaneous Identification of Structural Systems and Unknown External Inputs
The conventional extended Kalman filter (EKF) approach is only applicable when the information of external inputs to structures is available. Some improved methodologies with different complexities have been proposed in the last decade, but previous approaches based solely on acceleration measurements are inherently unstable which leads to drifts in the estimated unknown inputs and structural displacements. Although regularization schemes or post signal processing can be used to treat the drifts, they are not suitable for the real-time identification of structural systems and unknown inputs. In this paper, it is aimed to directly extend the conventional EKF for real-time simultaneous identification of structural systems and unknown inputs. Based on the procedures of the conventional EKF, an extended Kalman filter with unknown inputs (EKF-UI) is directly derived. Moreover, data fusion of partially measured displacement and acceleration responses is applied to prevent in real time the previous drifts in the estimated structural displacements and unknown inputs. Several numerical examples are used to demonstrate the effectiveness of the proposed EKF-UI for real-time identification of linear or nonlinear structural systems and unknown external excitations
Design and Validation of a Smartphone Based Wireless Structural Vibration Monitoring System
Traditional structural health monitoring (SHM) system usually uses expensive professional sensors, data transmission and data acquisition systems. Thus, only very important structures are affordable to install a SHM system. In recent years, smartphones become very popular, which not only integrate high-performance vibration sensors, but also have powerful data acquisition, processing, storage and transmission capabilities, making it possible to utilize smartphones to carry out some SHM tasks. In this paper, the authors developed a software that can easily convert several android based smartphones into a wireless structural vibration monitoring system. The software consists of a server program and a client program. The smart phone installing the server program can remotely control the smartphones installing the client program via local WIFI network to perform the tasks of network timesynchronization and measuring structural responses. First, the architecture of this program is introduced in detail. Then, the time synchronizing algorithm for all client nodes in the software is discussed. Next, the accuracy of time synchronization of the smartphone based wireless sensing system is validated through an experiment. Finally, a shaking table experiment is conducted, the results of which demonstrate that the smartphone based wireless structural vibration monitoring system can quite accurately identify the modal parameters of the tested structure.
Effectiveness of the Random Decrement Technique in Modal Identification of Structures using Ambient Vibration Response
The random decrement Technique (RDT) is a time domain procedure, where the structural responses to operational loads are transformed into random decrement functions, which are proportional to the correlation functions of a system’s operational responses, which can be considered equivalent to free vibration responses. Ambient vibration test of a structure usually produces noisy response and the existing modal identification techniques often fail to produce accurate results in such case. Due to contamination of ambient vibration with white Gaussian noise, preprocessing may be required to clean up the vibration signal in order to detect the modal properties accurately. In this paper, the Random Decrement Technique is used as a preprocessing technique to remove the redundancy form the data by use of cross correlation in Ambient Vibration test. The method has been tested for a steel cantilever beam having a length of 1000 mm rectangular cross section with 10 mm height and 65 mm width. The beam was instrumented with 5 wireless sensors placed at every 200 mm to measure the acceleration response. Both ambient and forced vibration tests were conducted for the beam. As expected, the modal identification method failed when the ambient vibration response was used without preprocessing. When preprocessed with RDT, the ambient vibration response produced modal properties similar to that obtained using forced vibration response.
Experimental Tests of Substructure Approaches for Health Monitoring System
Two substructure approaches combined with different statistical techniques are presented in the paper to reduce the challenges in the health monitoring system, such as the computation complexity, the number of unknown parameters and the number of sensors. In the approaches, a complete structure is divided into smaller substructures which are modelled as a series of single-degree-of-freedom (SDOF) systems by manipulating the equations of motion of the substructures. Newmark’s method is utilized to construct the discrete systems only containing accelerations from the continuous systems. The structural parameters are then separately identified in each system using statistical techniques. A new substructure approach incorporated with the constrained least square algorithm is proposed by the authors. This new approach along with the previous substructure approach based on the ARMAX models are both evaluated and compared in two laboratory experiments including system identification of a three-story structure and damage detection of a two-story structure. All the possible ways to segment the shear structure into substructures are also investigated in the study, where the substructures with lower model errors are selected. The proposed substructure approaches are able to estimate structural parameters every two seconds, and the structural performance is evaluated through the rates of changes of structural parameters. A real-time structural health monitoring (SHM) system can be realized based on the proposed substructure approaches with processing several accelerations each time.
Model-free Structural Hysteretic Force and Mass Identification using Limited Acceleration Measurement
Structural hysteretic behavior provides a direct description on the initiation and development of structural damage and can be used for a quantitative evaluation of structural energy consumption under dynamic loadings. Most of the current vibration based structural identification approaches using structural modal parameters usually need the known mass information and are applicable for linear system only. In this study, an iterative structural hysteretic force modeled with a Chebyshev polynomial and mass identification approach using limited acceleration measurements under incomplete excitation condition with the help of Unscented Kalman Filter (UKF) is proposed and validated numerically. A multi-degree-of-freedom (MDOF) structure equipped with a Magneto-rheological (MR) damper mimicking nonlinear behavior is established. Acceleration response time history at limited DOFs of the structure under dynamic excitation at a certain DOF is employed to identify the structural hysteretic force and mass. The influence of the acceleration measurement noise and the initial assumption on the mass is also considered and the identified nonlinear restoring force of the MR damper is compared with it from its numerical constitutive model. The identified mass of each DOF of the nonlinear MDOF model is close to its true value. Numerical results shows that the hysteretic force and mass distribution are identified with acceptable accuracy by the direct use of partially known noise-polluted acceleration measurements.
Performance, Reliability and Validation of Mass Spring Systems by Means of Vibration-based Excitation Sources
This paper presents the experimental evaluation of insertion loss of mounted track systems. Research outcomes can be implemented for the performance monitoring of metro lines and of high-speed railway infrastructures. Measurement-based identification of the systems has been developed following the “left and right test procedureâ€ according to DIN SPEC 45673-3 on three mockups under loaded and unloaded conditions. The mock-up represented a standard track, a light mass spring system (LMSS) and a heavy mass spring system (HMSS) of a metro line. An impact hammer with mass of 8.0 kg capable of exciting a maximum force of 22.5 kN is used as excitation source and nine vibration receivers are used according to several protocols. Time, frequency and time-frequency analysis of all the collected data is presented. Advanced signal processing coupled with filtering techniques has been applied to guarantee the reliability of the data. Scope of the tests is the determination of ground transfer and of natural frequency of loaded and unloaded tracks in order to calculate the characteristic insertion loss. Ground correction curves for the LMSS and the HMSS system are computed and implemented for the final results. Environmental and operational effects are also considered to properly prognose and manage the performance and reliability of the systems. Theoretical insertion loss curves for the HMSS and LMSS systems have been computed according to a 2D 6-degree of freedom numerical model and compared to experimental ones. In all cases, the experimental values are higher than theoretical ones.
Rapid Damage Assessment in Tall Buildings after Seismic Events
Rapid damage assessment in tall buildings is essential to confirm safety after seismic events. Structural Health Monitoring (SHM) offers the potential to provide this rapid assessment. However, the initial cost of SHM systems and subsequent maintenance can be prohibitive. This paper proposes a new approach for estimation of interstory drift responses in tall buildings using a limited number of accelerometers. The adequacy of using shear and Euler-Bernoulli beams to model tall buildings is also discussed. A Timoshenko beam model is proposed, and harmful interstory drift and interstory drift angle are defined as a damage sensitive features. A thirty-story building subjected to seismic excitation is considered to demonstrate the efficacy of the proposed framework. These results provide a strong foundation for rapid damage assessment in tall buildings after seismic events.
Research on Modal Analysis and Impact Force Inversion Based on Compressive Sampling Data
In recent years, many scholars have studied the impact force identification of large-sized structures in order to improve the safety of these structures, reduce the costs of maintenance and prolong the life. In the large-sized structural health monitoring, how to get a fast and accurate impact force is a worth further studying problem. Therefore, I develop a modal analysis method based on data in this paper, which replaces the modal analysis method based on finite element analysis, to solve the problem that it needs to rely on structural mechanics model in the traditional impact force inversion. In addition, I introduce the theory of Compressed Sensing (CS). Then I develop an impact force identification method based on CS, which includes the sparse model and the reconstruction optimization model. The method can overcome the problem that the data of storing and transmitting response are too large. Finally, a simulation experiment of large-sized aerospace aluminum plate is carried out to illustrate the impact force identification method. The results show that the method can identify quickly and accurately the impact force that was exerted on the large-scale structure
Sparsity-based Reconstruction Method for Simultaneous External Force Monitoring and Structural Damage Identification
Load monitoring and damage identification are important tasks in the field of Structural Health Monitoring. Reconstructing unknown force inputs or system parameters usually involves the solution of an inverse problem which is mostly ill-posed. In the last decades a lot of effort has been spent in solving these problems separately. However, unknown loads and damage both have influence on the structural vibration pattern. The difficulty of simultaneous identification of external forces and structural damage is to distinguish these influences by using only the measured vibration effects. The use of prior knowledge of the unknown quantities is advisable for solving the combined inverse problem and to obtain meaningful solutions. In this contribution a sparsity-based reconstruction method in time domain is developed for identifying the unknown structural force excitation and damage parameter simultaneously by using output-only acceleration data. Sparsity means the majority of the solution vector is zero and only a very few elements are nonzero. Here damage is interpreted as additional load on the damaged structural element (virtual distortion). A numerical proof-of-concept experiment of a quadratic aluminum plate is presented. It shows that the proposed reconstruction method is able to identify the unknown external force and damage parameter by using a significant lower number of accelerometers than present methods.
Speed Extraction from Vibration Signal using ANNs and Broadband Features
The authors provide a method based on ensembles of artificial neural networks (ANNs) that, fed with supervised training data, are able to estimate rotational speed of the machine under investigation. Ensembles of various ANNs trained using different algorithms provide reliability of the method and protection from unfortunate initial weights distribution. The method is validated on the basis of data gathered in large amount of experiments performed in epicyclic gearbox test bed
Structural Reliability Updating and Damage Assessment with Bayesian Networks
The traditional reliability analysis method cannot handle discrete variables and cannot give response timely when there are a new information (usually measurements) about the structure. In this paper, a computational framework for structural reliability updating and damage assessment is proposed. System modeling of traditional Bayesian network is developed with continuous variables discretization and elimination. Firstly the reliability Bayesian network (RBN) is established according to the structure type, and to discretize the key component information variables. Then the redundant continuous nodes are eliminated according to Shachter's node elimination rule. Final the variable elimination method is used to carry out the exact inference and calculate the posterior probability distribution. When evidence (measurement) is available, at forward inference, the proposed method can facilitates structural reliability updating, and at backward inference, the proposed method can carry out damage assessment for the selected key component. Taking a rigid frame as the research object. By comparison with Monte Carlo method, the reliability deviation is less than 5%, it shows validity and accuracy of the proposed method.
Vibration Analysis and Structural Optimization of Networking Equipment for the Internet of Things under Base Excitation
The Internet of Things (IoT) has been widely used in varieties of applications, including intelligent control and structural health monitoring. As the basic part of IoT systems, the underlying networking equipment has to be characterized with high reliability. However, unlike other traditional networking products, it always works at severe environments. Many reliability issues of hardware and software occur due to the complex working conditions. Among the environmental factors, vibration may cause contact problems between components, which can lead to system failure. Thus, system-level simulation and experiment are needed to monitor and enhance the structural health. In this paper, finite element (FE) simulation is performed to calculate the mode and harmonic response of underlying electronic equipment of IoT under base excitation, and experiments are carried out to verify the simulation results. Through the simulation and experiments results, the weak part of the whole system can be detected. Then an approach to strengthen the structure and reduce the vibration is put forward. Furthermore, the strengthened structure is validated by both simulations and experiments. The results show that the optimized networking equipment has smaller vibration amplitude and provides a safer environment for the inside components
Acoustic Emission for Monitoring the Elastic Concrete Beam Subjected to Three-point-bending Tests
The application of the acoustic emission (A processes and crack characterization in elastic c paper. Thirty-six elastic concrete three-point I technique. Four different rubber contents, O% consideration. Test results show that the AE ene significantly with the increasing rubber content. contents, the number gaps between two com occurrence means that more rubber particles m For high content of rubber particles, the top of t the load-time curve for low content. Two or mo AE energy curves. This phenomenon corresp particles can help concrete disperse into two ductility. The present experimental study measurement of fracture energy and the relatio energy of elastic concrete. An equation has been elastic concrete by knowing acoustic emission
Application of Shape-based Similarity Measures to Classification of Acoustic Emission Waveforms
Over the past decades, Acoustic Emission (AE) has emerged as a promising in-situ inspection technique in different applications such as civil or airborne structures, rotating machinery, or process monitoring. As AE refers to the sudden release of elastic energy due to structural changes in stressed materials (i.e. damage), the acquired waveforms are related to their underlying source mechanism. Thus, using suitable signal processing techniques, different source mechanisms can be distinguished. Despite encouraging results, processing of AE signals still remains challenging due to high sample rates. Therefore, in practical applications typically only few parameters (i.e. hit rate, rise time, maximum amplitude) are extracted using threshold-based approaches. Thus, storage requirements are greatly reduced. However, to develop an indepth understanding of AE source mechanisms, detailed waveform analysis is generally required. Here, frequency domain approaches are extensively used. While competing methods use different statistics, cross-correlation, or autoregressive coefficients, detailed investigation of raw AE waveforms in time domain has not received much attention. In time series data mining, shape-based distance measures are frequently used to assess the similarity of time series objects. Considering each time series as a high dimensional feature vector, the distance of two time series can be computed using Lp-norms such as Euclidean distance. This approach is highly attractive, as it is parameter- free. Due to strict point-to-point comparison these measures are sensitive to scaling and time warping (i.e. non-linear shifting on the time axis causing local stretching or compression of the signal). While the former can be addressed by suitable normalization of the data, Dynamic Time Warping (DTW) is frequently used to achieve invariance of distance calculations to warping. Thus, DTW allows compensating for misalignment of individual time series elements. In this work, experiments are performed using thin plates of composite material. Waveforms related to AE hits from different underlying source mechanisms (i.e. in-plane and out-of-plane source motion) are acquired. Results of hierarchical clustering and nearest neighbor-based classification indicate that shape-based distance measures are suitable to distinguish between different AE sources using raw AE waveform data.
Experimental and Computational Analysis of Acoustic Emission Waveforms for SHM Applications
Acoustic emission (AE) waveform monitoring has a great potential to use as an SHM technology. The design of an SHM system requires a thorough understanding not only in the AE hit level but also in the waveform level. Capturing the fatigue-crack generated acoustic waves from a thin aircraft material is a challenging task. The present work focuses on capturing the AE waveform from the fatigue-crack of the thin specimen. The capability of the piezoelectric wafer active sensor (PWAS), commonly used for SHM design, in capturing the fatigue-crack AE has been illustrated. The AE waveforms were captured from an in-situ AE-fatigue experiment. The AE waveforms were analyzed and two distinct types of AE waveforms were frequently observed. Multiphysics FEM simulations also showed the two types of AE waveforms corresponding to the two types of AE events. The local interaction of the AE waveform with the fatigue-crack was also observed that was yet to capture experimentally. The concept of seismological moment tensor seemed to work to simulate the AE source in the thin structure. It has been shown that different components of moment tensor can successfully simulate the different groups of experimental AE waveforms
Fiber-Optic Acoustic Emission Sensor based on a Chirped FBG Pair for Crack Detection in Aluminum Plate
In this paper, a fiber-optic acoustic emission (AE) sensor system for the detection of AE signals generated from cracks within an aluminum plate is described. The sensor head consists of a pair of tandem chirped fiber Bragg gratings (CFBGs) that form a Fabry-Perot type interferometer (FPI). This CFBG-FPI features a series of resonant wavelength notches in the reflection spectrum, which are subject to the same wavelength shift as the sensor is stretched or compressed by AE signals. By locking a tunable laser to the slope of any individual notch, the AE induced high-frequency wavelength shift is converted into intensity variation. Using the sensor system, AE signals generated by three different types of sources, i.e., PZT actuator, pencil break, and cracks within aluminum plates, are detected and compared. Our experimental results suggest that cracks in the aluminum plates gave birth to broadband AE with peak intensity spanning over 100 kHz to 350 kHz.
In-situ Wear Monitoring: An Experimental Investigation of Acoustic Emission During Thread Forming
To meet todays economic requirements, continuous supervision of machining processes becomes increasingly important. In this context, estimation of tool wear is considered a difficult task due to complex interactions between tool and workpiece as well as noise originating from the environment. Besides different process parameters such as torque, thrust, or temperature, Acoustic Emission (AE) has emerged as a promising technique for indirect assessment of tool wear in different machining operations such as drilling or turning. Here, AE source mechanisms can be related to plastic deformation, chipping, or elastic interaction of surfaces. Thread forming is a transformative manufacturing process for generating threads in ductile materials. As the thread geometry is manufactured by cold forming of the material, lubricating properties of the Metalworking Fluid (MWF) strongly effect tool wear and workpiece quality. In this work, AE measurements are conducted in context of thread forming. A tribometer is used to carry out thread forming trials under well-controlled experimental conditions. Different MWFs are considered, including lubricating oils as well as water-based MWFs. Time-frequency domain analysis of the AE signal is applied using continuous wavelet transform. Here, different process phases, forward and reverse, can be distinguished according to peak frequencies of the AE signal. Furthermore, using k-means clustering it can be shown that AE energy in different frequency bands can be used as a feature to distinguish different MWF qualities. According to the results, lower energy levels of the AE signal using high performance lubricating oils indicate a dependency between wear rates and quality of the MWF. Altogether, AE appears as a promising technique to develop in-depth understanding of wear mechanisms related to friction contact of tool flanks and workpiece.
Increased Lamb Wave Detection Sensitivity of Surface Bonded Fiber Bragg Gratings through Bonding Optimization
Current adhesive bonding approaches for fiber Bragg grating (FBG) sensor networks to detect Lamb waves and acoustic emission (AE) signals in naval structures follow the same guidelines as for quasi-static strain detection. These bonding strategies favor thin, stiff adhesives to maximize the quasi-static displacement coupling between the optical fiber and substructure. However, in this paper, we demonstrate that following the bonding design guidelines typically applied to piezoelectric (PZT) transducers for Lamb wave and AE signal detection can significantly increase the output signal amplitudes of FBG sensors to these waves. Simulations of the wave propagation through various adhesive bond conditions provide validation of the observed behaviors.
Source Identification and Classification of Acoustic Emission Signals by a SHAZAM-inspired Pattern Recognition Algorithm
This study focuses on the development of a source identification algorithm inspired by the SHAZAM music app. The algorithm makes use of a spectrogram analysis technique for distinguishing different Acoustic Emission (AE) events. The peaks of the spectrogram are used to obtain a constellation map generating a “fingerprintâ€ like pattern for each acoustic emission source. The fingerprints are then used within an artificial intelligence algorithm as part of a Knowledge Discovery database. The database is then able to link the AE signal to a specific source type. An experimental program was developed to test the methodology. The results of this study demonstrate that signal sources can be classified and linked to specific emission types with a high level of accuracy
System for Logging Acoustic Emission Signals from FRP Reinforcing Rods that is Suitable for Field and Long Term Laboratory Testing
Fibre reinforced polymer (FRP) rods have seen increasing use in civil infrastructure applications. FRP rods are corrosion resistant and are easier to handle than steel. They have been used in over 190 projects including many bridge decks and traffic barriers. However, at sufficiently high loads (more than ~25% of the ultimate load) FRP rods can become susceptible to creep failure. In addition, FRP rods have linear stress strain relationships up to failure and hence mechanical property changes to do provide a reliable indicator of failure. To study and monitor failure in FRP rods acoustic emission has been used as a diagnostic tool. Acoustic emission signals characteristics can be correlated with the level of stress within the FRP rod. For example, signal entropy has proven to be a good indicator of the stress level in glass and carbon FRP rods. However, due to the high sampling rates in AE signal acquisition, signal analysis has required post processing of signals. For longer term field and long-term laboratory studies, it is not practical to record and post process the data from the AE signals, which is produced at several megabytes per second. In this work we describe a system that uses triggered recording of the AE signals so that only the signal for a few milliseconds are logged in the vicinity of each AE event. The system is microcontroller based and allows for sampling at ~700k samples per second, with logging to SD cards. The system is suitable for deployment at a large number of nodes. Examples of the system being applied to AE monitoring of glass FRP rods will be shown. The system will be used for longterm laboratory studies of FRP rods at low load levels (less than 50% of ultimate).
A Method to Monitor the Circular Deformation of Metro Shield Tunnels in Soft Soils
With regard to the metro shield tunnels in soft soils, large circular deformation, such as large longitudinal joint opening and horizontal diameter convergence, is a main cause of water leakage and is detrimental in tunnel operation. Implementing the circular deformation monitoring system contributes to the safe operation and longterm maintenance of tunnels. Although there are many ways to obtain deformation information, measuring inclination of segments is one of the most effective ways, and it is convenient to derive the deformation from the inclination data. A real-time monitoring system using wireless sensor network for circular deformation monitoring has been developed and deployed in an section tunnel of Shanghai metro line 13. The monitoring section is between West Nanjing Road and Natural Museum Station and characterized by densely surrounding buildings and nearby large foundation pit excavation. Under the above project background, the field monitoring data are analyzed and presented to evaluate the circular deformation. Based on compatibility of deformation, the simplified formulas are proposed to derive the horizontal diameter convergence using the inclination variation of four lining segments. Data acquired through total station is compared with the proposed method to verify the accuracy of the monitoring system. The results indicate that the monitoring system is reliable, reasonable and can be further applied, and the difference between proposed method and manual data is in an allowable variation range
Optimized Structural Health Monitoring System Design for Aviation Structures
This paper describes the development and testing of an active-acoustic-structuralhealth- monitoring (SHM) methodology for detection of crack damage that occurs in aircraft structures. The SHM techniques employed measure and evaluate frequency dependent mechanical responses. The responses are from a monitored structure excited by acoustic/vibratory signals injected into the structure at preselected locations. The SHM methodology included the following stages: (1) a design stage, where researchers conducted dynamic-digital simulations, based on a finite element model (FEM) of a subject airframe structure, determining favorable locations on the structure for placement of acoustic actuators and sensors; (2) a baseline testing stage, where test engineers installed acoustic actuators and sensors on an actual helicopter airframe structure in accordance with the design stage findings; then, conduct repetitive frequency-sweep tests collecting acoustic baseline response data; (3) a damaged-structure testing stage, where test engineers induced specific crack damage in the structure, then repeated the tests of the baseline testing stage, acquiring corresponding data for the damaged structure; and, (4) an analysis stage, where the researchers employed statistical-anomaly-detection techniques identifying statistically-significant differences between the baseline data and the damage test data to determine detection rates and localization performance.
Structure Health (SH) sensing technologies selection is driven by stress requirements, what must be detected and allowable size of anomalies. This paper presents integrated Structure Health (SH) sensing technologies answering to several challenges. Top level challenges are considered such as: design optimization, integration into component during manufacturing or assemblies, and durability demonstration. All must sustain profitable and innovative maintenance operations. Two technical solutions described in this publication cover damage detection, fatigue follow up, and corrosion detection. One system is made of an optical wave guide network including different optical fibre technologies already well known with added innovations. The second is based on electromagnetic technology known as Eddy Current, well known in the field of classical non-destructive testing addressing corrosion and deep crack detection
An Introduction to the Heterogeneous SHM System for a Super-large-scale Multiple-tower Suspension Bridge
The Taizhou Yangtze River Bridge (TZB), located in China, is the first three-pylon suspension bridge with the main spans over two kilometers in the world. After its construction in 2012, a heterogeneous structural health monitoring system (HSHMS) combining wireless and wired techniques was implemented for the structural health assessment of the bridge. A total of 275 sensors comprising of accelerometers, strain sensors, three-dimensional ultrasonic anemometer, thermo-hygrometer, global positioning system and temperature sensors were installed to collect structural and environmental parameters for the assessment. In this paper, the structural uniqueness of the TZB and its HSHMS were introduced first. As a landmark project, wireless smart sensors were adopted in the vibration monitoring of the suspension cables. Critical structural parameters variations were obtained based on the data analyses. The results show that HSHMS and wireless smart sensors are promising for the structural health assessment of the unique suspension bridge.
Bridge Impact Detection and Classification using Artificial Neural Networks
Low-clearance railroad bridges are susceptible to impacts from trucks that exceed the required clearance. These impacts can be detected by mounting instrumentation on the bridge, such as accelerometers and inclinometers, and analyzing data from these sensors. A train running over a bridge also generates a significant response, and the bridge responses produced by trains are not always dissimilar to those produced by vehicle impacts. Understanding whether an event is produced by a vehicle impact or a train is critical; a vehicle impact should be investigated, while a train crossing the bridge is part of a bridge’s regular operation. This paper presents a system of artificial neural networks capable of examining response time histories and determining if the response is a train or an impact. Event data obtained from sensor modules mounted on several diverse railroad bridges has been analyzed to identify key signal characteristics, train neural algorithms, and classify events. Signal characteristics of importance include time and frequency content and a signal center of mass metric. Combinations of these characteristics have been used to improve impact detection on specific bridges to 66%- 97% and reduce false positive rates to 0.09% - 1.82%.
First Steps Toward Integration of Structural Health Monitoring and Non-Destructive Evaluation Data for Decision Making
In order to bring objective and mechanistic approaches to the repair and retrofit of highway bridges, multi-source data should be collected and properly integrated to provide effective bridge condition assessment to allow better asset management decisions. Although information from multiple Structural Health Monitoring (SHM) and Non-Destructive Evaluation (NDE) approaches may lead to more data and possibly to more reliable condition assessment, it may often not prove economical to deploy several monitoring or non-destructive methodologies. Visual inspection is usually the first and often the only step to periodically obtain information on bridge conditions (such as visible corrosion, cracks and vibration) and possibly observe the causes leading to deterioration of highway structures (such as trapped water, road salt, temperature, live loads, wind and others). Meanwhile visual inspection has some known limitations; for instance, it cannot commonly recognize unknown reserve capacities nor discover deficiencies that are not visible (e.g. delaminated deck) leaving much uncertainty in the actual condition and performance of existing bridges. It has been advocated for many years that in conjunction with visual inspection, objective in-depth assessment technologies such as monitoring operational responses and NDE should be deployed. However, when multiple sensing approaches are used, an integration of the collected data becomes necessary for proper interpretation and decision making. Effective integration of information collected from different SHM and NDE sources can be very challenging. This study focuses on optimal and practical ways to obtain and integrate measurement data with other proxy information (such as past inspection and maintenance records, traffic information, National Bridge Inventory (NBI) data, climate and weather data and others). Such integration strategies, if proven effective, could lead to more reliable interpretations of bridge condition and performance, and benefit highway asset management.
Damage Detection and Deteriorating Structural Systems
This paper addresses the quantification of the value of damage detection system and algorithm information on the basis of Value of Information (VoI) analysis to enhance the benefit of damage detection information by providing the basis for its optimization before it is performed and implemented. The approach of the quantification the value of damage detection information builds upon the Bayesian decision theory facilitating the utilization of damage detection performance models, which describe the information and its precision on structural system level, facilitating actions to ensure the structural integrity and facilitating to describe the structural system performance and its functionality throughout the service life. The structural system performance is described with its functionality, its deterioration and its behavior under extreme loading. The structural system reliability given the damage detection information is determined utilizing Bayesian updating. The damage detection performance is described with the probability of indication for different component and system damage states taking into account type 1 and type 2 errors. The value of damage detection information is then calculated as the difference between the expected benefits and risks utilizing the damage detection information or not. With an application example of the developed approach based on a deteriorating Pratt truss system, the value of damage detection information is determined, demonstrating the potential of risk reduction and expected cost reduction
Novel Detection and Quantification of Sensitivity of Impact-Induced Structural Dynamics: Potential Enhanced Damage Detection by Wave Chaos Information
In an effort to develop advanced diagnostics (damage detection and system identification) for structures of complicated geometry, we have focused on an important source of uncertainty in experimental structural dynamics. Wave chaos can occur in linear structures because of the phenomenon of ray splitting, a potential source of uncertainty. The investigated sensitivity is on the initial local velocities induced by nondestructive interrogating impacts with a modal hammer. We have detected and computed spatial distributions of regular and irregular sensitivity of transient dynamics developed in the domains of two prototypical structural systems. The novelty of the present work resides in the use of the proper orthogonal decomposition (POD), also known as principal component analysis (PCA), transform to fuse raw databases (data sets) in the form of (1) raw collocated acceleration signals (CAS) and (2) raw distributed acceleration signals (DAS) of the transient response. The extracted, after the data fusion, POD modes are used as a basis for analytics aimed at detecting and quantifying sensitivity hidden in the raw data sets. We claim that the irregular sensitivity of the transient dynamic is perhaps the shadow of chaotic scattering of elastic waves by the damaged regions. The physics phenomenon of chaotic wave and particle scattering occurs in diverse physical systems ranging from quantum mechanics (cavities for instance) to continuum small scale elastic systems
On-board Decision Making Platform for Structural Health Monitoring
The ability to monitor the health of complex structures such as aeronautic or civil engineering structures in real time is becoming increasingly important. This process is referred to as structural health monitoring (SHM) and relies on onboard platforms comprising sensors, computational units, communication resources, and sometimes actuators. Many of such platforms have been developed within the last years but there is still a lack of structuration and knowledge exchange regarding the software and hardware architectures of such platforms. The aim of the present paper is to introduce an open hardware and open software platform dedicated to SHM within the fields of aeronautics and civil engineering. The platform presented here will be made available in an open hardware and open source framework to allow SHM researchers to run concurrent detection, localization, classification or quantification algorithms using simple interpreted languages such as Python.
Optimal Sensor Placement and Scheduling with the Value of Information
The management of civil infrastructure involves accounting for uncertain or variable factors which influence the performance of a system. These factors can vary in space over the domain of the system and in time as the system changes. Effective decision-making for system management should be guided by models which account for uncertainty in these influencing factors as well as information gathered about the system to reduce this uncertainty. Value of information provides a rational metric for quantifying the benefits of information gathering efforts to support system management decision-making. However, the computation of this metric in spatially and temporally extensive systems presents a practical impediment to its implementation. In this paper, we investigate a special case of system topology, termed as a temporally decomposable system with uncontrolled evolution, in which the computational complexity of value of information evaluation grows at a manageable rate with respect to the problem time horizon. We demonstrate the evaluation of the value of information to support the design of a structural health monitoring scheme, using data collected for the Scott Hall building at Carnegie Mellon University. We also investigate the relative benefits of online sensor placement, i.e., of having the ability to revise sensor placements and/or schedules over time as information is gathered within a system.
Parametric Analysis of Value of Information for Monitoring Infrastructure Components
Information collected by monitoring systems can provide a significant economic benefit to the operation and maintenance of infrastructure components only under specific conditions. Not only that information has to be precise and useful for relevant decision problems, e.g. for scheduling maintenance and repairs, but the decision maker has to be able to process that information and react in due time. Moreover, the detected phenomena do not have to be predictable without the monitoring system; otherwise the information would be redundant. Formally, all these considerations can be embedded in the Value of Information (VoI) metric, a utility-based metric for assessing the impact of data in decision making under uncertainty. For computing the VoI, we predict the outcomes of sensors, the reaction of controllers and the overall cost, and compare it with the expected cost without using the monitoring systems. In this paper, we investigate the relation between the VoI and key features of the monitoring systems, of the component deterioration and of the decision-making process, including sensor availability, precision, degradation rate, damage predictability, reaction time, repair costs, and economic discount factors. Leveraging previous work, we model the maintenance process as a Partially Observable Markov Decision Process (POMDP), and we compute the VoI of long-term monitoring. The parametric analysis allows us to identify the optimal amount of information to be collected (without over or under-instrumenting components) and, e.g., to compare the VoI of frequent imprecise measurements with that of rarer but more precise ones. Overall, our framework is useful for identifying conditions when the benefit of monitoring is high, and it can be applied to a broad range of infrastructure systems.
Progress of the COST Action TU1402 on the Quantification of the Value of Structural Health Monitoring
This paper summarizes the development of Value of Structural Health Monitoring (SHM) Information analyses and introduces the development, objectives and approaches of the COST Action TU1402 on this topic. SHM research and engineering has been focused on the extraction of loading, degradation and structural features for damage detection and condition assessment, system identification and model updating. However, there is an actual challenge to establish a better understanding of the value of SHM before its implementation in terms of its utility in conjunction with practically applicable methods for its quantification. This challenge can be met with Value of SHM Information analyses facilitating that the SHM contribution to substantial benefits for life safety, economy and beyond can be may be quantified, demonstrated and utilized. However, Value of SHM Information analyses involve complex models encompassing the infrastructure and the SHM systems, their functionality and thus require the interaction of several research disciplines. For progressing on these points, a scientific networking and dissemination project namely the COST Action TU1402 has been initiated.
Quantifying the Benefit of SHM: What if the Manager is not the Owner?
The research community generally recognizes the Value of Information (VoI) as a realistic indicator of the benefit of the information provided by a monitoring system in structures management. Even though many authors have recently focused on the assessment of the VoI, most of their works do not consider the presence of two individuals in the decision process of whether to install a monitoring system, the owner and the manager of the structure. In this contribution, we define a framework to quantifying the VoI of a monitoring system by considering these two separate individuals, who have a different aversion toward risk. Moreover, we apply the results on a real-life case study concerning the Streicker Bridge in Princeton, NJ, in the USA. This framework aims to help the owner in quantifying the money saved by entrusting the evaluation of the state of the structure to the monitoring system, even if the manager’s behaviour toward risk is different from the owner’s own, and so are his or her management decisions. The results of the case study confirm the difference in the two way to quantify the VoI of a monitoring system.
Reliability Monitoring for an Individual Product Based on Degradation Information Fusion Reliability Monitoring for an Individual Product Based on Degradation Information Fusion
Degradation analysis has become very useful in reliability monitoring and maintenance schedule making for complex systems and highly reliable products with long lifetime. It is suggested, in practical engineering, that more attention should be paid to the time-varying health state and reliability index for key service-products from the viewpoint of safety and life-cycle cost. In this study, the degradation information of both product level and component level is combined and adopted to assessing the time-varying reliability of an individual product. A novel nonstationary independent increment degradation model including both deterministic and stochastic parameters is proposed to depict the statistical property evolution over time. The product-level degradation data and the component-level degradation data are incorporated for the maximum likelihood estimation (MLE) of model parameters. Then the reliability characteristics can be described by updating the stochastic model parameters based on product-level degradation information via a Bayesian method. Consequently, the remaining useful life (RUL) and the mean time to failure (MTTF) can be estimated and updated once a new online performance measurement is obtained. Therefore, the time-varying reliability can be monitored for an individual product
Technology Advancements Lead to New Approaches in Structural Health Monitoring (SHM) with Battery-free Wireless Embedded RFID Strain Sensors
Recent advancements in RFID sensing technology have led to a wide variety of new, battery-free wireless sensors. One major application of battery-free RFID wireless sensors has been embedded strain sensors in structural health monitoring (SHM) applications. These new RFID sensors resolve the challenges of SHM strain monitoring that previous technologies faced
Wireless Sensors Synchronization: An Accurate and Deterministic GPS-based Algorithm
Last decade, the need for wireless sensors solutions as core-solutions of Structural Monitoring gained in interest. The cost of wireless devices compared to the cost of wiring important structures (bridges, energy-plants, etc.) is attractive. Most of recent development in WSN domain focused on energy (saving or harvesting), on wireless proto-cols, on embedded algorithms. But it is a fact that, most of monitoring applications need samples to be time-stamped. According to the application, the wished time resolution could be up to one second for automation monitoring, one millisecond for vibration, one microsecond for acoustic monitoring, one nanosecond for electricity or light propagation... The consequence for a Wireless network of electronic nodes is that, by nature, no common signal could physically provide a synchronization top. But, as each electronic device, a wireless sensor time-base uses a timer incremented by a quartz whose initial value is theoretical up to some p.p.m. and whose period drift on time because of age, temperature, etc. Two kind of solutions could be regarded: a synchronization signal provided by the wireless protocol itself; an absolute synchronization from a referential source such as: GPS, Frankfurt clock, Galileo, etc. In the first way, it will be demonstrated the poor accuracy and the need of energy such a mechanism offers. In the second way, the article will details how a deterministic (Universal Time), accurate and resilient algorithm has been implemented. The article also provides specific results of application on acoustic monitoring system and electricity propagation where the accuracy of a WSN has reached up to 10 nanosecond UT. Consequence on energy consumption of this algorithm are given with a description of future works to improve the energy balance while keeping the device sober and synchronized.
Coupling In Situ Microstructure Observation with Machine Learning Algorithms for Damage Diagnostics and Prognostics
Acoustic Emission (AE) obtained from a novel experimental setup which involves recording of AE data from inside a Scanning Electron Microscope (SEM), is leveraged in this article in a signal processing approach that combines data reduction, classification and outlier analysis to identify data trends and relate them to detection of damage and predictions of remaining useful life at the specimen level. This approach offers the possibility to increase the reliability of NDE methods used in data-driven characterization of material behavior. To demonstrate the benefits of this approach, two types of Aluminum alloys, 2024-T3 and 7075-T651 were mechanically tested inside a SEM while simultaneously recording AE. Data obtained by this approach was then used to develop and validate a data reduction and classification algorithm with the goal to identify sensing information that appears to be the most sensitive to the activation of microstructural-level damage mechanisms. By combining this approach with an outlier analysis it is demonstrated that it is possible to eliminate noise and provide data trends that are found to follow the material degradation process in both monotonic and cyclic conditions. The framework presented can be extended to a variety of materials, while the produced data trends may serve as inputs to computational models.
Development of Strain Measurement Based On-line Impact Monitoring System for Composite Structures
The high specific strength and tailorable properties have made advanced composites structures to find increased use in the aircraft industry. Impact damages due to events such as tool drops, runway debris etc. can reduce the load carrying capacity of composite structures and impair safe operation of the aircraft. Often they result in barely visible external damage and as such they may get missed during visual inspection of large aircraft structures. Impact events are random in nature, in both time domain (uncertain when it will occur) and spatial domain (where it will occur), hence, the challenge is to develop a smart impact monitoring system, which will remain active to report the impact event on the structure. This paper describes the experimental studies carried out towards the development of an online impact monitoring system, which can estimate the impact location and severity of impact in terms of impact energy/force. Such a system can assist in carrying out inspection only in the identified locations instead of inspecting the whole structure. An impact test setup developed in-house is used to carry out impact tests on composite structures. The portable impact tower comprises of a drop weight and a tup instrumented with a force sensor to measure the force response during impact. Sensor sensitivity studies are carried out to determine the optimum sensor locations on the structure. Fiber Bragg Grating (FBG) and Resistance Strain Gauges (RSG) sensors are used in this study to measure strains in the structure during impact. A comprehensive method based on a building block approach is followed. Several laminates of different thicknesses are tested first before moving on to more complex configurations such as skin-stringer panels and wing-box like structures. These studies allow us to understand the complexities in the structural response towards impact and help in design of sensor networks as well as algorithms for estimation of impact location and severity. In order to determine the impact location using strain data acquired during the impact event, various algorithms are developed, validated and implemented in LabVIEW platform. Support vector regression and system identification techniques based algorithms are used to determine the impact severity. Comparative results of the algorithms will also be discussed through Probability of Detection/Confidence.
Embedded Sensing of Damage in Composite Materials
The integrity of composite structures gradually degrades due to the onset of damage such as matrix cracking, fiber/matrix debonding, and delamination. Over the last two decades, great strides have been made in structural health monitoring (SHM) community using various sensing techniques such as acoustic emission, eddy current, strain gages, etc., to diagnose damage in aerospace, mechanical and civil infrastructures. Embedded sensing offers the prospects of providing for real-time, in-service monitoring of damage were weight savings is a major factor in Aerospace Industry. In this present work, magnetostrictive particles such as Terfenol-D were embedded in a composite structure, along with multiple SHM techniques, to capture the damage in an IM7-carbon fiber reinforced polymer composite system undergoing fatigue loading. As the internal stress state increases, the change in the magnetization flux intensity was captured using a non-contact magnetic field sensor. A damage diagnosis system was established along with an acoustic emissions technique to further validate the damage captured by the embedded system. The goal of this project is to identify the change in the mechanical and magnetic property within a composite material during the evolution of damage. Several characterization techniques will be used to determine interfacial fiber-matrix interactions which will provide for a more comprehensive understanding of the composite interfaces.·
Ensuring the Reliability of Damage Detection in Composites by Fusion of Differently Classified Acoustic Emission Measurements
The overall system reliability of complex or safety critical systems is of increasing importance. Composite materials are increasingly used instead of conventional construction materials due to advantageous mechanical properties, such as high specific strength resulting from their complex structure. Therefore, different nondestructive testing techniques, which indicate developing damages in composites, are proposed for continuous monitoring to ensure reliability and safety. Here passive emitted Acoustic Emission (AE) in combination with frequency analysis is used to identify characteristic features related to different failure modes of composites. The use of different filtering and classification methods to identify damages in composites results in various performance of classifiers. Ensuring high accuracy of the decisions evaluating faults, system changes or even the State-of-Health, the assignments from different classifiers evaluating the same situation can be fused to one final decision stating a similar statement but with improved probability of belief. Some factors just influencing only one individual filtering or classification can be compensated. Using ensemble selection strategies, only suitable classifiers are selected to be considered in the fusion process. In this contribution specific selection strategies and fusion algorithms are used to combine results of differently classified Acoustic Emission measurements to evaluate and distinguish different kind of damage of composite structures. The individual properties of suitable classifiers to be fused as well as the kind of selection are crucial for the overall accuracy. The results show the advantages of the combination of different assignments to ensure the reliability of a monitored system as well as the SHM system for monitoring.
Heterodyning Effect in Composites Bond Inspection
With daily increase of application composite materials in commercial and military aircrafts, longevity of composite bonds has become a substantial factor in aerospace industry. Although tremendous amount of research has been conducted on monitoring the integrity of composite bonds, developing new structural health monitoring (SHM) techniques for evaluating bond strength could be essential. In this study, heterodyning method is implemented for exploring bond strength and detecting delamination in composite materials. Heterodyne effect may be utilized for the evaluation of debonded area when the linear characteristics of the system changes to nonlinear. The light contact in the bonding zone can result in this conversion from linear to nonlinear behavior. The nonlinear system responds to two appropriate excitation frequencies with new frequencies. For some nonlinear systems, the output has these new frequencies at the subtraction and summation of excitation frequencies. This is called heterodyne effect. This experimental setup was designed to detect debonding in a composite coupon. In the experiment, two composite coupons are manufactured and bonded together by using film adhesive. To create a 2-inch-long debonding, no adhesive was applied in the left side of the bonded coupons. Three piezoelectric disks were attached to the bonded coupons while the middle disk acting as the exciter. The objective of the experiment was to study the alteration of the signals received by the piezoelectric disks attached to the opposite ends due to nonlinear behavior of the structure. The experimental setup was designed for the investigation of undesirable bonding conditions. Since the method evaluates the bond strength by detection of the changes of linear characteristics of the system to nonlinear, there is no need for baseline data. Results show that the proposed method can be used effectively for the composite bond inspection purposes.
Online Remaining Useful Life Prognosis for Composite Materials Based on Acoustic Emission and Strain Data
The procedure of fatigue damage accumulation in composite structures, is a complex phenomenon due to the multiphase nature of composites, the variation of inherent manufacturing defects, the randomness of loads, the stochastic activation of different damage mechanisms and an incomplete knowledge about the physics behind the evolution and interaction of damage mechanisms. In order to develop a robust model, which will reliably estimate the remaining useful life of the structure, we hypothesize that the damage accumulation process is of a stochastic nature and we propose a framework that combines a stochastic model with structural health monitoring data. In this study, the Non-Homogenous Hidden Semi Markov model and acoustic emission and strain data extracted from fatigue tests of open-hole quasi-isotropic carbon fibre reinforced polymers are selected. The damage accumulation is a hidden process and the goal of the framework is to correlate the structural health monitoring data with the parameters of the model and identify the hidden relationship. In conclusion, the remaining useful life estimations of each structural health monitoring technique are compared with the actual remaining useful life and based on performance metrics it was found that the strain data provided more accurate predictions.
Validating 3D Photonic Crystals for Structural Health Monitoring
A photonic crystal (PhC) is a periodic structure with nanometric periodicity comparable with the wavelength of light, having a photonic band gap in the visible range: in practice, it reflects selectively only a band of the incident light, thus appearing to the observer of a determinate color. In this contribution, we propose to use photonic crystals as a colorimetric sensitive material for Structural Heath Monitoring. The idea is based on the observation that any distortion in the crystal structure produces a change in the reflected light bandwidth, resulting in turn in a change in its apparent color, visible to naked eyes. In a near future, we will be able to speed a photonic sensitive material on the surface of a structure in the form of a thin paint layer, and directly measure any variation in the strain field by simply observing change in color. To demonstrate this concept, we first we introduce the basic formulation that controls the photo-mechanical behavior of a 3D photonic structures. Next, we demonstrate the feasibility of the fabrication of a PhC made of sub-micrometric polystyrene colloidal spheres in a PDMS matrix on a rubber substrate. Through laboratory experiments, we show that the photonic properties of the crystal change with substrate elongation according to theoretical prediction. Lastly, we introduce a Finite Difference Time Domain (FDTD) analysis method to simulate the opto-mechanical response of a generic photonic crystal design, through direct integration Maxwell’s equations, and validated the method compering the numerical results to the experimental data
A Novel Two-dimensional Distributed Temperature Sensor Based on Electrical Resistance Tomography
This paper presents the development of a novel two-dimensional (2D) Electrical Resistance Tomography (ERT) based distributed temperature sensor. This sensor consists of a thin layer of porous metal film that is manufactured by spraying colloidal copper paint to the surface of an electrically non-conductive substrate. A local temperature change of the metal film, locally changes its electrical conductivity. This conductivity change is localized and quantified with ERT. In the present work, the proposed temperature sensor is experimentally evaluated by imposing known temperature changes on its surface using a heat source. The results indicate that the ERT-based temperature sensor provides a good qualitative estimates of the magnitude and quantitative estimates of the location of the temperature change.
Damage Detection in a Vibrating Structure using Distributed and Dynamic Fiber-Optic Techniques
This paper reports on the application of three fully distributed and dynamic fiberoptic strain measurement techniques for the detection of damages in a vibrating cantilever beam, where measurements are taken while the beam is in motion, thereby imitating the dynamic environment of airborne structures and that of other scenarios as well. Judicious placement of the fibers helps damage-induced strains to appear as deviations from a zero-strain baseline, while most strains related to damage-free normal motions are filtered out. Such clear damage signatures enable a more accurate assessment of the damage size and location
Damage Detection in Composite Pipes During Mechanical Three Point Bending
Improvements in the design and manufacturing process for composite materials have allowed them to become a cost-effective replacement for metals in structural applications. The cost of inspection, however, continues to represent a large proportion of the life cycle costs of composite components. Since composite materials allow for the integration of sensors, with negligible effect on their mechanical properties, permanent structural health monitoring (SHM) systems have sparked a great deal of interest. The aim of this work was to combine SHM and conventional nondestructive techniques (NDT) to detect and localize the initiation and progression of damage in a tubular specimen during several cycles of flexural loading/unloading. Active infrared thermography (IRT) is used in the reflective mode to scan the specimen between loadings; from this data, thermal diffusivity mappings of the specimen are generated. Two different SHM techniques are used during mechanical loading: (i) acoustic emission (AE) event monitoring using surface mounted piezoelectric sensors, and (ii) distributed strain monitoring using surface mounted optical fibre sensors. Distributed strain data suggests that the loadings are elastic, and residual strains do not appear to be present. The collected AE data suggests significant damage on first glance, however more thorough analysis reveals that the signals are caused primarily by matrix cracking that is damage but not as critical as delamination or fibre breakage.
Field Study on Wireless Sensor Networks for Structural Health Monitoring using Beamforming Communication Systems
In industrial environments, monitoring data from sensor nodes is typically transferred using wire-based field-bus systems. In environments where cable-based systems cannot be employed, battery-powered wireless sensor nodes offer an alternative solution. In our analysis, we elaborate in how far spatial multiplexing using millimeter wave phased-array antenna systems have advantages over omnidirectional communication links, with special focus on power consumption and reliability. Furthermore, we have compared surface-mount sensor nodes using 60 GHz communication links to classical transmission systems in terms of system design and power efficiency. In addition to the related works, our analysis includes a proposal for a network hierarchy and outlines the real-time capability and power consumption of mm-Wave sensor networks for the purpose of structural health monitoring of buildings, bridges or other objects.
Development of the Fiber Optic Distributed Strain Sensing System and its On-Board Monitoring of Aircraft during Flight Tests
In this study, we develop a fiber optic distributed sensing technique to conduct aircraft structural monitoring during flights. The sensing system is based on optical frequency domain reflectometry that is capable of monitoring strain distributions with mm-order spatial resolution in real-time. We use fiber Bragg gratings (FBGs) with the maximum length of 5 m gauge length as a sensing element. We conduct flight tests using a jet flying test bed (Cessna Citation Sovereign). We bond the FBG to a stringer of the fuselage, and interrogate by the sensing system installed in the cabin. We monitor and record the real-time strain distributions on-board during flight tests. The system successfully monitored structural responses of the aircraft under various flight conditions.
Dynamic Reconstruction of In-plane Strain Maps using a Two-dimensional Sensing Skin
Damage detection and localization in large-scale systems requires the detection of local faults over a typically very large geometry. This can be done through the deployment of twodimensional sensor arrays capable of discreetly monitoring local changes over a structure’s global area. The authors have previously developed a soft-elastomeric capacitor (SEC) thin film sensor capable of measuring the additive strain components over a surface along its two principal directions. For applications to structural health monitoring, it is useful to decompose the signal into strain components along these principal directions. When deployed in a dense sensor network configuration, the principal strain components can be extracted from the measured additive strain using a previously developed algorithm that used assumptions on the boundary conditions. The introduction of mature off-the-shelf solutions, for the purpose of boundary condition updating, has been shown to significantly improve the algorithm’s accuracy. The newly created hybrid dense sensor network (HDSN), consisting of SECs and resistive strain gauges, is capable of producing orthogonal strain maps over the structure’s surface. Previous studies were conducted using static loads. Here, the authors report on the capability of the HDSN-based technique to reconstruct dynamic strain maps for a component exposed to dynamic loads. Results demonstrate that the hybrid dense sensor network can efficiently reconstruct dynamic in-plane strain maps. These dynamic strain maps can then be used for the reconstruction of dynamic deflection shapes or, in combination with other damage detection algorithms, to detect, localize and quantify damage.
Impact Damage Detection and Localization of Adhesively Bonded Fiber Reinforced Structures using Silver Nanoparticle Based Printed Circuits
Electrical conductivity of carbon fibers and carbon nanotubes provides the possibility of in situ strain monitoring and damage detection of composite structures by electrical resistance measurement. To exploit this property and use the material itself as a sensor in a structural health monitoring system a reliable contacting of the material is needed to enable electrical resistance measurement during operation. Due to its high potential for industrial automation and its excellent reproducibility, ink-jet printing serves as promising technology to print circuits and realize contacting on the monitored material. The study deals with a new structural health monitoring approach using inkjet printing of silver nanoparticle based ink directly onto the composites structure. Impact damages are introduced into adhesively bonded glass fiber reinforced polymer test specimens. A modification of the epoxy based adhesive films with carbon nanoparticles allows for electrical resistance measurements through the bonding. The introduced impacts are investigated using non-destructive testing methods, i.e. ultrasonic inspection, and light microscopy. Electrical resistance measurement shows the possibility of accurate damage detection and damage localization in one or two dimensions, depending on the conducting path designs.
Load and Deformation Identifications of a Wing Structure by using Distributed Fiber-Optic Sensors
In this study distributed fiber-optic sensors based on optical frequency domain reflectometry (OFDR) were applied to identification of load and deformation in a wing structure made of aluminum alloy. Optical fibers with the length of more than 2 m were bonded as the sensing fiber on the both sides of the wing, respectively. The sensing fiber traveled back and forth twice on each side to measure strain distributions along the wing with the length of 700 mm. We could obtain the Bragg wavelength along the optical fiber with continuously-written low-reflectivity fiber gratings from the sensing system. In order to apply distributed loads to the wing, we used a towing tank. The wing was fixed on a carriage to form a cantilever beam. We towed the wing in the water as changing the angle of attack and the towing speed. We could observe bending deformations and successfully measure strain distributions along the sensing fibers at the high spatial resolution and identify the deformation and load applied to the wing
Local and Global Approaches for Damage Detection in Composite Structures by Fiber Optic Sensor
Fiber optic sensors cannot measure damage directly; for getting information about damage from strain measurements, additional strategies are needed, and several alternatives have been proposed. This paper discuss two different concepts: the first one is based on detecting the new strains appearing around a damage spot; the structure does not need to be under loads; the technique is very robust, damage detectability is high, but it requires sensors to be located very close to the damage, so it is a local technique. The second approach offers a wider coverage of the structure; it is based on identifying the changes caused by the damage on the global strains field in the whole structure for similar external loads. Damage may happen anywhere in the structure, detectability is dependent upon the sensors network density, damage size and the external loads. Examples of application to real structures are given
Monitoring Early-age Strain and Temperature Distributions in Full-scale Steel-concrete Composite Beams with Distributed Fiber Optic Sensors
Concrete is subject to temperature gradients and shrinkage-induced strain during curing. Strain variations in concrete can lead to residual stresses and cracks, in particular when the deformation of concrete is restrained. This in turn can affect the mechanical performance and durability of the structure. In this study, a fullydistributed fiber optic sensor is used to monitor the early-age temperature and strain distributions in a 12.8 m long steel-concrete composite beam based on pulse pre-pump Brillouin optical time domain analysis. Telecommunication-grade, single-mode optical fibers were embedded in the concrete of the specimen that consisted of lightweight aggregate concrete on trapezoidal metal deck acting compositely with a steel I-beam via headed shear studs. Emphases were placed on characterizing the temperature and strain distributions in concrete and understanding the effects of composite action on the evolution of strains in the concrete. Non-uniform strain distributions were successfully measured from the distributed fiber optic sensors in the first two days of curing. The spatial distribution of temperatures along the beam was relatively uniform but varied in amplitude due to the hydration reactions. The strain distributions were non-uniform, which is likely due to concrete cracking as the result of restrained shrinkage of the concrete.
Monitoring Method and System for Uneven Sedimentation of Storage Tank Foundation Based on FBG
Uneven sedimentation of the oil storage tank foundation beyond standard design requirements will not only destroy the tank structure and the foundation itself, which leads to the tank media leakage will also cause adverse environmental impact, and cause huge economic losses, so uneven sedimentation of storage tank foundation has always been an important issue in storage tank management. Based on the safety characteristics of large oil storage tanks, this paper puts forward the method of monitoring the sedimentation of the storage tank foundation using FBG static leveling sensor, analyzes the principle of FBG static leveling sensor, and designs the tank sedimentation monitoring system which include sensing layer, network layer and application layer. A field test was carried out to investigate the developed system at the repository, 55 sensors of 9 oil storage tanks are made up of the sensor network based on the probabilistic model, and the total number is 43800 for five months. It shows that the system can real-time give uneven sedimentation trend of the storage tank foundation, realize on-line continuous monitoring, and meet the requirements of actual measurement. The system will provide data support for the geological evolution and forecast of the repository to ensure the safe operation of the oil storage tank. At the same time the technology and the system also can be applied to new tanks filling water test for tank foundation sedimentation observation
Optical Fiber for 3D Imaging of Deformations in Concrete Containers Stacked
long-term management of all radioactive waste. Andra is developing a 500-metre deep disposal concept for High and Intermediate Level Long Lived Waste (HLW & ILLW). For the ILLW, the primary nuclear waste packages will be enclosed in reinforced concrete containers before final storage. Such kind of concrete waste container must fulfill appropriate specifications. For instance, three containers could be stacked on top of each other. As each container loaded by its primary waste package weights about 15 tons, and in order to verify that this capability reveal very little deformation, a full scale test has been set up. Two samples of concrete containers have been instrumented by two optical fiber sensing cables to provide distributed strain measurements and eight vibrating wire strain gauges. These sensors have been fixed on the reinforcement bars before the concrete pouring. An Optical Backscatter Reflectometer (OBR) connected to the optical fibers provides fast measurements of deformations all along the fiber with a centimeter spatial resolution (inside the container). An adequate positioning of the optical fibers with regards to the vibrating wire strain gauges has revealed not only the little deformations at the stacking step but also when the containers are lifted up. This paper presents tools and methodology of the test. It describes the experimental results and comparison to the data resulting from a modeling (FEM).
Railway Wheel Defect Identification using the Signals Reconstructed from Impact Load Data
Wheel Impact Load Detectors are common devices that measure the rail response made by the wheel-rail contact to estimate the condition of the in-service wheels. The data collected by the multiple sensors can be fused to reconstruct a wheel-rail contact pattern over the circumferential coordinate that provides some description of the wheel condition. Moving of a defective wheel with different velocities and axle loads influence the wheel-rail interaction and the pattern reconstructed. As a result, there is a range of variation in the pattern reconstructed for each defect. Therefore, this paper aims to tackle this challenge and to classify the railway wheel defects using pattern recognition tools. Due to the lack of real data, ADAMS/Rail is used to model the wheel-rail contact and to simulate the data collected by the sensors. Then, based on the fusion algorithm, the condition state signals are reconstructed for different wheel defects with different velocities, and axle loads. Then a dataset based on these patterns is generated that is used for training, and testing the classifiers. In this paper, the magnitude of the signal is directly used as the features. The results of the classification show that the wheel defects including a minor defect can be correctly classified with zero error
SHM using Integrated Photonics Based Fiber Sensing Solutions
Structural Health Monitoring (SHM) aims to give, at every moment during the life of a structure, a diagnosis of the “stateâ€ of the constituent materials, of the different parts, and of the full assembly of these parts constituting the structure as a whole. Current State of the Art Fiber Sensing solutions based on Photonic Integrated Circuit Technology are commercially emerging and developed towards innovative environmental compliant implementations such as for Aerospace. Miniature, solid state optical chip based modules are classifiable as high reliability components and of specific interest to environments that intrinsically subject to SHM activities. With these qualifiable solid state modules a versatility of applications can be supported through different markets. Aerospace calls for SHM capabilities in damage and impact detection, localization and identification, shape monitoring of morphing aircraft structures, high resolution distributed sensing of extreme dynamic load assessments of complex structures. The ability to combine a versatility of measurement capabilities (i.e. resolution and sample frequencies) proves an incredible appearance of applications that become possible that could not be addressed by conventional techniques. This paper addresses this potential of Integrated Photonics based fiber sensing systems for SHM capabilities for Aerospace, supporting the ever growing need for improving system performances, reduced costs and materials, and reduced system footprint, hence improving economic viability of things.
Strain Field Identification of an Aluminum Plate by using Distributed Fiber-Optic Sensors
In this study we are aiming to identify strain fields of a simply-supported thin plate structure which is subjected to perpendicular loads. In this case a strain field means three strain components, namely e , e , and g , on a surface of the plate. Firstly, an area of a plate is discretized into a number of square elements. Distributed fiber-optic sensors were applied to determine the strain field at each element and the strain field distribution in a whole plate is reconstructed by polynomial equations with the coefficients determined by a least-square method. In various load cases we could successfully identify the strain field at discretized elements on an aluminum plate and reconstruct the strain field distribution in a whole plate.
Strain Transfer for Optimal Performance of Sensing Sheet
Sensing sheets based on Large Area Electronics (LAE) and Integrated Circuits (ICs) are novel sensors designed to enable reliable early-stage detection of local unusual structural behaviors. Such a device consists of a dense array of strain sensors, patterned onto flexible polyimide substrate along with associated electronics. Previous tests performed on steel specimens equipped with sensing sheet prototypes and subjected to fatigue cracking pointed to a potential issue: individual sensors that were on or near a crack would immediately fail, and could not be used to assess the size of the crack opening or to monitor the future crack growth. In these tests, a stiff adhesive was used to bond the sensing sheet prototype to the steel specimen. Such an adhesive provided excellent strain transfer, but it also caused premature failure of individual sensors within the sheet. Therefore, the aim of this paper is to identify alternative adhesives that alleviate stress concentrations in the sensing sheet at a crack, and provide strain transfer that is sufficient for reliable early-stage crack detection.
Associated Data Analysis and Application Based on the Stress Measurements
Stress is the essential measurand for the structural health monitoring system of the spatial steel structures, which can be usually used to evaluate the strength of the structural member directly and the safety of the whole structure indirectly. The strain sensors are placed to collect the measurements, while the effectiveness of such headend sensors is the key point to ensure the whole structural health monitoring evaluation based on the stress measurements be in normal working condition. This paper focuses on the inherent correlation relationship of structural stress measurements, the associated data analysis is given and the sensor diagnosis is given as the application of the associated data analysis. The stress measurements collected from the structural health monitoring system of Shenzhen Bay Stadium are used as an example to illustrate proposed associated data analysis and its application, which can be the references for the association analysis and its application of the similar large span spatial structures.
Virtual Environments for Structural Health Monitoring
While thorough documentation has always been a crucial feature of life cycle monitoring and assessment, different forms of documentation have not always been well integrated. Thorough documentation can include 2D images, 3D models, architectural drawings, reports from past interventions, current monitoring data, among other elements. In life-cycle monitoring, it is important that all of these types of documentation are well integrated and organized. The work presented here reflects a methodology and digital workflow for organizing and integrating existing documentation and data about a structure into a virtual reality environment. Through presenting data about a structure in an intuitive, organized, and interactive way, a virtual environment will foster communication among different groups working on a project, and greatly help the engineers to quickly and comprehensively assess the condition of the structure. The virtual environment proposed here uses 360 degree spherical imaging (Ricoh Theta) and virtual environment software (Kolor Panotour Pro) to aid a user in virtually experiencing a structure accompanied by its associated data. This project was implemented on Streicker Bridge, a 350 foot pedestrian bridge, in Princeton, NJ, USA. Since construction began in 2009, this bridge has been monitored using fiber-optic strain sensors. This data is incorporated into the virtual environment to highlight the possibilities of this method for life-cycle management. The lessons learned will be recommendations for how virtual environments can be used to enhance current practices and visualization methods for long-term monitoring and assessment.
WiStealth: A Fixed Point RSSI Covert Channel using Multi-Modality Filtering Techniques
Wireless networks are abundant throughout public spaces and medical facilities. Health monitoring systems may employ the use of wireless communication for data observation and collection in a Dynamic Data Driven Application System (DDDAS). Receive Signal Strength Indicator (RSSI) is a metric to evaluate the performance of two wireless devices and it can be leveraged as a medium for a covert channel. This paper proposes a novel approach in utilizing the observation of RSSI measurements at a wireless receiver as a covert channel. By fluctuating the transmitting power of a wireless emitter, the peak and troughs of an observation can be evaluated as a message. Additionally, RSSI as a metric is significantly hindered by noise and interference. Thus, the hindrance presents a challenge when RSSI is employed as a covert channel. To address such a challenge, we apply wavelet transforms to denoise the measurements of RSSI for when we fluctuate the transmitting power. Our realworld evaluation demonstrates the applicability, accuracy, and performance of the proposed approach in DDDAS
Intelligent Flight State Identification of a Self-Sensing Wing through Neural Network Modelling
With the development of micro-fabrication techniques, multi-functional sensor networks have been created with many micro-sensor nodes for various functions. An UAV wing is integrated with a stretchable sensor network including piezoelectric transducer, strain gauge and resistance temperature detector embedded near the wing surface and this novel design intends to equip the wing with the self-sensing capability similar to bird feathers. One of the main challenges of the state-of-the-art research is how to make the wing aware its flight states in real-time through large amount of sensing signals. Features are extracted and further selected for neural network modelling. Both classic neural network and deep belief network are proposed respectively to map the relationship from the sensing data to physical flight states and compare the identification accuracy with each other. The simulation results show a relatively high identification accuracy of the proposed methods, enabling new perspectives in developing intelligent self-awareness capabilities for next generation smart wings.
Multiscale DDDAS Framework for Damage Prediction in Aerospace Composite Structures with Emphasis on Unmanned Aerial Vehicles
In recent years, there has been a significant increase in the use of Unmanned Aerial Vehicles by the US military. UAVs are expected to fly a large number of long (48 or more hours) missions, and operate without failure. Furthermore, in order to increase the durability of these vehicles and to decrease weight, composite materials are currently experiencing a widespread adoption in applications related both to military and civilian aerospace structures. As a result, in order to decrease costs associated with the operation, maintenance, and, in some cases, loss of these vehicles, it is desirable to have a Dynamically Data-Driven Application System framework that can reliably predict the onset and progressions of structural damage in geometrically and materially complex aerospace composite structures operating in the environments typical of UAVs. In this work we present a multiscale DDDAS framework for damage prediction in aerospace structures with emphasis on self-aware air vehicles
Structural Assessment and Sensor Placement Strategy for Self-Aware Aerospace Vehicles
This paper addresses real-time structural health assessment as a form of aircraft selfawareness. Limited time and resources available on-board and incomplete measured data affected by uncertainty are the key challenges to face. We discuss a data-driven methodology that combines Multi-Step Reduced Order Modeling to support structural self-awareness, and unsupervised learning (Self-Organizing Maps) to identify optimal sets of sensor locations. In particular, two implementations of our sensor placement strategy are presented and compared for a composite wing panel subjected to a number of damage conditions.
Data-driven State Awareness for Fly-by-feel Aerial Vehicles: Experimental Assessment of a Non-parametric Probabilistic Stall Detection Approach
In this work, an experimental study of a novel data-driven fly-by-feel state awareness method is presented. A non-parametric probabilistic approach for stall detection is investigated and assessed via a series of wind tunnel experiments. The method is based on the statistical analysis of the recorded signals and subsequent statistical hypothesis testing and decision making procedures. In this proof-of-concept experimental study, the flight state is defined by two variables, the airspeed and angle of attack. The experimental evaluation and assessment is based on a prototype bio-inspired self-sensing composite wing that is subjected to a series of wind tunnel experiments under multiple flight states. Distributed micro-sensors, in the form of stretchable sensor networks, are embedded in the composite layup of the wing in order to provide the sensing capabilities. Experimental data collected from piezoelectric sensors are employed for the development and assessment of a non-parametric fly-by-feel stall detection approach within a probabilistic framework. In this study, special emphasis is given to the early detection of aerodynamic stall without making use of any conventional information related to the attitude of the vehicle. The method is able to provide in real time the probability of stall for an indicative flight scenario that was implemented in the wind tunnel. The obtained results demonstrate the effectiveness and potential of the developed approach
A Reference-Free Guided Wave Based Damage Localization Approach for Highly Dispersive Structures
The detection and localization of in situ damage in complex sandwich material such as X-COR sandwich composites using guided wave based structural health monitoring techniques is difficult due to the highly dispersive media and complex geometry, which complicates the interpretation of the reflected wave modes induced by damage. In this paper, a novel reference-free damage localization framework is developed, whereby converted wave modes are studied using their time-space representations constructed from sensor arrays signals. The source of each wave mode is subsequently localized using linear regression techniques. The X-COR sandwich structure utilized in this research was manufactured to include seeded facesheet delaminations. Macro-fiber composite sensors were bonded to the panel surface and used to validate the developed framework. Excellent agreement is shown between the predicted and actual locations of delamination and actuator. The results indicate that mode conversion is an effective damage indicator, and the proposed time-space based damage localization framework is a promising structural health monitoring tool for highly dispersive media.
Application of Model Assisted Probability of Detection (MAPOD) to a Guided Wave SHM System
This paper updates continued development on a study to generate Probability of detection (PoD) and Receiver Operating Characteristic (ROC) curves for a guided wave (GW) Structural Health Monitoring (SHM) system. PoD curves are used to access a detector’s performance as a function of damage size or equivalently the backscattered Energy to Noise Ratio (ENR). ROC curves present statistical representations of the reliability of the method for detecting certain size flaws versus their Probability of False Alarms (PFA). To a large extent, generating meaningful versions of these curves involves multiple repetitions of the same experiments while injecting as much realistic variability as possible. This paper examines the impact of a few key variables such as averaging and distance to flaw and how they can be applied to model assisted PoD
Compact Laser Ultrasound Scanner for Wide-Area Persistent Monitoring
In this study we evaluate the feasibility of using a simple, unstabilized, homodyne interferometer configuration for local wavenumber estimation of guided waves, a process known as acoustic wavenumber spectroscopy [1]. Acoustic wavenumber spectroscopy identifies damage in a two-dimensional scan on a pixel by pixel basis, by estimating the wavenumber of a structure’s response to a steady-state, single frequency, ultrasonic excitation. By leveraging the requirement to measure only a single known frequency it may be possible to use a simplified laser doppler vibrometer (LDV). A simple and inexpensive laser ultrasound scanning device would provide convenient and rapid stand-off inspection for evaluating structural damage in a variety of settings. Due to the typical cost, complexity, and size of commercial LDV systems, they are often only used for one-off measurements in the laboratory. A simplified system could enable permanent deployment of LDV technology for persistent structural health monitoring applications.
Compressive Sensing and Local Wavenumber Estimations for Fast Damage Imaging with Guided Waves Inspections
Many nondestructive evaluations and structural health monitoring techniques rely on the analysis of guided waves propagation in large regions of plate like structures, but such ultrasound inspection techniques are often quite slow and require human interaction. For these reasons, extracting information related to the damage condition of a structure can be a time consuming process. Therefore, it is necessary to have fast and accurate techniques for damage detection and quantification. This research describes a method using compressive sensing (CS) and local wavenumber estimation techniques that can lead to fast scanning and damage detection procedures. The compressed sensing technique reduces the amount of measurements needed, thus achieving faster scanning. In this paper, it is demonstrated that such procedure does not compromise the detection accuracy, as a matter of fact, it allows to improve the performance of damage imaging tasks by removing noise artifacts. In the experiments detailed in this work, guided waves are excited with a piezoelectric transducer bonded to the inspected structure and sensed by an air-coupled probe mounted on a CNC machine for horizontal and vertical scanning. Full wavefields are rapidly reconstructed by applying the compressive sensing technique. Then, local wavenumber domain analysis is performed for processing guided wavefield data. To image the defect itself, we employed the application of both compressive sensing and local wavenumber damage quantification techniques to guided wavefield data for fast damage imaging process. To demonstrate the effectiveness of the proposed techniques, several experiments were performed on aluminum structure, emulating defect with a mass. The results demonstrate that the techniques are very effective in localizing damage with high inspection speed by sampling just the 10% of the Nyquist scanpoints.
Damage Detection in a Composite T-Joint using Guided Lamb Waves
An integrated network of lead zirconate titanate (PZT) piezoelectric transducers could be used to monitor a structure over time for life prognosis through generation and sensing of guided ultrasonic waves. The aim of this study is to develop such a sensing method for damage assessment in a composite T-joint and subsequently follow its evolution under mechanical loading. In this context, monitoring of Lamb waves in a composite T-joint has been completed where dispersion and tuning curves have been obtained. Guided waves are transmitted into the structure through different pairs of transducers in a pitch-catch active structural health monitoring (SHM) approach. With these experiments, Lamb wave modes have been identified and a baseline achieved, that will be used in the near future to reliably detect impact damage
Damage Detection in Complex Composite Material Structures by using Elliptical Triangulation Method
This paper is concerned with the detection and characterization of impact damage in stiffened composite panels using elliptical triangulation method and high frequency Lamb waves. Laminated composite materials, used in many aerospace and aircraft structures, often contain a variety of hidden internal flaws which occur during manufacturing and processing of the material or while the structure is subjected to service loads. In order to avoid quick failure of structural components, a special attention was given to the damage detection in complex structures. More specifically, structural health monitoring based on the vibration of structures has been at the focus of attention of many researchers in order to obtain very efficient damage detection techniques. In this way, few nondestructive techniques based on measurement of Lamb waves were developed. On the same approach is based the elliptical triangulation method presented in this paper. Through the calculation of the Time of Flight of the signals and the knowledge of the actuator-sensor positions, the method allows to identify position and dimension of the damage. The effectiveness of the elliptical triangulation method was highlighted through a preliminary numericalexperimental correlation on flat plates. Subsequently, the method is applied to a stiffened structures in composite material affected by the impact damage. Upon the numerical characterization of the damage entities generated by low-velocity impacts at 15J and 30J on the stiffened structures, the waves propagation analysis were performed on both undamaged and damaged structures. The feasibility of the ellipse triangulation method on the evaluation of the position and the damaged area on complex structures realized in composite material has been characterized. The potential applications of the methods in developing health monitoring systems in defects-critical structures and in complex composite material structures are discussed.
Experimental Analysis of Digital Data Communication in Intelligent Structures using Lamb Waves
This paper presents the experimental proof of concept for spread spectrum digital data communication in intelligent structures using guided ultrasound waves (GW). We are able to show the feasibility of the CDMA-like communication scheme by means of the fundamental GW-modes that are propagating in an aluminum plate. The first goal is to use a simulation approach to study multibit propagation and to analyse the maximum data rates that can be achieved by the proposed communication link as a function of frequency. Secondly, we perform an experimental analysis in which we observe resonance phenomena of the piezoelectric transducer at specific frequencies which allow us to transmit data bits with much lower energy compared to data communication at other frequencies. The data communication concept presented here can be used in autonomous sensor nodes that support damage detection and data communication at the same time. This potentially will eliminate the need to use cable connections, e.g. to send damage indicators, which is often a main drawback of GW-technology in aircraft structures
For the Practical Use of a Lamb Wave-based SHM System
We have been developing a Lamb wave-based structural health monitoring system which can contribute to achieve cost reduction of aircraft maintenance, more efficient operation of aircraft in airlines, and future design improvement for aircraft manufacturers. In our SHM system, Lamb waves are generated and measured with micro actuators and optical fiber sensors, which are installed in aircraft structures. When damages initiate and grow in a structure, Lamb waves which propagate thorough the structure are influenced and changed by the damages. By analyzing the changes of the measured Lamb waves, the SHM system can detect the damage in the structure, consequently, can evaluate structural integrities. We have conducted several verification tests, taking the environmental effects into account so far. In this paper, investigation results of simulation on propagation behavior of Lamb waves and results of flight tests were reported.
Ground Penetrating Radar for Material Characterization in Structural Health Monitoring Applications
Unusual behavior in reinforced concrete structures, including those instrumented with embedded sensor networks, can often be detected by structural health monitoring (SHM) methods. This behavior can be caused by corrosion, cracking, delamination, or construction that differs from the design specifications. It is necessary to investigate unusual behavior to determine its cause. Nondestructive investigations are often applied to validate conclusions drawn from SHM methods, i.e. localization and description of features. Ground penetrating radar (GPR) has been used to investigate defects in structures, but has yet to be used to investigate the influence of material or mechanical properties of materials for the purposes of SHM. In this work, a post-tensioned concrete pedestrian bridge instrumented with fiber optic sensors was investigated (1) to quantify the differences between the concrete used in each of the two construction phases and (2) to locate and map continuous internal features of the bridge deck. A 2.6 GHz GPR antenna was used to survey the main span and two of four approaches of the bridge (one of which was constructed in the second construction phase). In addition to analyzing the data with standard processing and filtering techniques, additional attributes of the data were analyzed to quantify the difference between the concrete strength of each construction phase. The difference in mechanical properties of the concrete in each phase has been the subject of numerous SHM studies on the bridge and has been confirmed during this investigation. This discrepancy agrees with standard lab testing of concrete cores at 7 and 28 days, which is advantageous in associated SHM and analysis of the bridge. The results explore the relationship between electromagnetic and strength characteristics of the concrete, as well as the value of using GPR to complement SHM sensing approaches in understanding anomalies and as-built conditions of a structure.
Guided Wave Generation and Propagation in Piezoelectric Composite Plates for Establishing Structural Self-awareness
This paper presents a numerical investigation of guided wave generation, propagation, interaction with damage, and reception in piezoelectric composite plates for the purpose of establishing structural self-awareness. This approach employs piezoelectric composite materials as both load bearing structure and sensing elements. To understand the wave propagation characteristics in piezoelectric composite plates, finite element modal analysis of a plate cell with Bloch-Floquet condition is performed. A comparative study is carried out between a standard composite plate and a piezoelectric composite plate to highlight the influence of piezoelectricity on guided wave dispersion relations. Subsequently, a transient dynamic coupled-field finite element model is constructed to simulate the procedure of guided wave generation, propagation, interaction with damage, and reception in a piezoelectric composite plate. Active sensing array is designed to capture the structural response containing the damage information. Three engineering scenarios are considered to demonstrate the ultrasonic sensing capability of the piezoelectric composite system: a pristine case, a one-damage-location case, and a two-damage-location case. Finally, time-reversal method is utilized to locate and image the damage zones. This research shows that piezoelectric composite material possesses great potential to establish structural selfawareness, if it serves both as the load bearing and structural sensing component.
Guided Wave Mode Selection for Microstructure-sensitive Mutual Wave Interactions
The generation of waves from the mutual interaction of two ultrasonic guided waves has strong potential for early detection of material degradation. Selection of primary wave modes and frequencies that interact to generate propagating wave modes is discussed and two examples of experimental results are described. The results show that the generated waves propagate on their own and are sensitive to microstructural evolution in fatigue.
A Fundamental Study on Characterization of Guided Wave Scattering from Crack under Different Loading Conditions
Ultrasonic Guided wave based inspection technique is one of the useful damage detection techniques. Waves are introduced using Piezo-electric Wafer Active Sensors (PWAS) sensors, which are placed, at discrete locations in order to inspect damages in plates and the idea has the potential to be extended to assembled structures. Various actuator-sensor configurations are possible within the network in order to identify and locate damages. The ability to detect the smallest defects within a material domain depends upon the wavelength of the incident wave, wave propagation characteristics, noise, and uncertainty in various quantities of interest involved. However, complex geometric features in a component also contribute to mode conversions and scattering losses, which make it difficult to distinguish the signal packets in a time signal received at a receiver location. In a component with multiple rivet holes such as fuselage of an aircraft, one of important type of damages that are required to be inspected carefully in such assembled structures are the rivethole cracks and corrosion. Ultrasonic inspection is performed by measuring and characterizing the waves reflected or transmitted through the damage. With no apriori knowledge on orientation of such damages, but only probable zone of high importance of inspection prescribed from the design criteria and loads on the component, the inspection method requires employing complex signal processing algorithms to analyze the data and identify the effect of scattering. In addition, the damages cause varying degree of changes in ultrasonic wave signals. The signals change significantly when crack from a rivet is opened up under load whereas in unloaded condition, cracks are often hard to detect In this study we present the effect of crack under fatigue load on the signal parameters during different loading conditions. We first introduce a single hole on the component and then a fatigue crack is allowed to grow from the hole. We show the effect of growing crack on the ultrasonic signals through amplitude and phase modulation factors. This describes the effect of growing crack with respect to a reference signal measured before the crack was grown.
Guided Wave Tomography for Corrosion Monitoring in Planar Structures
Guided wave tomography offers a quantitative and very robust way of monitoring corrosion in planar structures. We explore here different tomography algorithms and compare their performances to reconstruct an extended area of corrosion (represented by a continuous thickness reduction) in an aluminum plate. These studies are performed on both experimental and simulated data. Simple tomography algorithms, such as SART, are based on a straight ray propagation assumption and requires the determination of the time of flight between all the couples of the sensor’s distribution around the monitored area. However, in practice, refraction caused by the defect must be taken into account which is done by allowing a curvature of the rays (bent-ray algorithm). A recent algorithm, called HARBUT, can deal with the diffraction phenomena, but requires not only the time of flight but the whole signal between the different couples of transducers. The range of validity of these imaging algorithms (e.g. with respect to Born approximation) are explored using simulations performed with a spectral finite element formulation offered within CIVA simulation platform developed at CEA. Practical recommendations are given on the inspection intervals, based on the expected corrosion increasing rate, in order to maximize the quality of the reconstruction. Tomographic reconstructions based on experimental results are also presented.
Identification of Impact Damages in Aircraft Composites by Nonlinear Guided Wave Technique
To maintain the best possible structural integrity of composites, it is highly desirable to detect the impact damage at the smallest possible scale. Nonlinear ultrasonic guided waves technique has been accepted as a useful tool for evaluating and detecting the micro-damages in early stage. Considering that second harmonics could provide information sensitive for structural health condition, this paper investigates an experimental scheme for measuring the micro-damages caused by low-velocity impacts in carbon/epoxy composites. The degree of injury is demonstrated with the impact energy. The present results show a monotonic increase of acoustic nonlinearity with respect to the increase of impact energy. This relevance between the acoustic nonlinear parameters and impact energy indicates that the use of nonlinear ultrasonic guided wave can be a sensitive means for the low-velocity impact damages in composites
Low Temperatures Influence on the Lamb Waves in Aluminum Structures
their capability to detect and localize online and offline defects in the structures. The performance of this method such sensitivity and the precision for defect localization could be influenced by environmental parameters. One of the most important of these parameters is the temperature. It is affecting the whole chain of the defect detection method, from wave generation to signal acquisition. The temperature can affect piezoelectric actuators and transducers electro-mechanical performances and the mechanical coupling between them and the monitored structure and also the mechanical guided waves propagation parameters thru the structure. We have performed and experiment for establishing how big is this influence on the whole system. The structure used is aluminum plates which has piezoelectric actuators and transducers (PWAS) glued to them on different distances between them. Each of these PWAS are used as transmitter or as receiver at a time for generating or for receiving the Lamb waves thru structure. The signal acquisition has been done for few frequencies such as S0 and A0 dominant frequencies and other values between them on temperatures between 00C and -700C. The reason for choosing this temperature range was due to the fact the most aircrafts are flying on high altitude where these temperatures values are common. These results shows important changes of the received signal with temperature variation. This is an important fact which needs to be taken into consideration for structure health monitoring for the optimization of the method used.
Multichannel Device for Integrated Pitch Catch and EMI Measurements in Guided Wave Structural Health Monitoring Applications
This paper presents a device allowing both electromechanical impedance and pitchcatch measurements on multiple channels for structural health monitoring (SHM) applications. Temperature sensors are included for distributed temperature sensing to compensate the temperature effect from the ultrasound signals to enhance the damage detection robustness. The inclusion of an embedded linux system allows remote deployment as well as on-the-fly calculations on the device, e.g. for sensor self-sensing tasks. We provide a detailed description of the devices functionality as well as an evaluation of the EMI capabilities by studying a carbon fiber reinforced plastic (CFRP) tube structure, as a demonstrator structure of a ships rudder shaft, and calculating damage indicators for a drill damage.
Numerical and Experimental Study of First Symmetric and Antisymmetric Lamb Wave Modes Generated and Received by Dual-PZTs in a Composite Plate
Structural Health Monitoring is a multidisciplinary field that provides processes whose aim is to monitor damages within structures. Most damage indexes are calculated using Lamb wave signals generated and received by surface-mounted piezoceramic transducers (PZT). The symmetric and antisymmetric Lamb wave modes have different velocities and tend to highlight different kinds of damages. In most SHM applications, a narrowband tone burst with a single frequency component is used as excitation signal. Using two dual PZTs (concentric disc and ring) allows one to tune the emitted signal properly in order to favor antisymmetric or symmetric mode propagation, and to decompose both mode contributions in the received signal. Hence those easy-to-settle devices enable one to detect small damages with appropriate frequencies while guarantying optimal energy efficiency in emission and reception. In this article, a simple model based on the Kirchhoff-Love beam theory is used to estimate the amplitude of the S and A waves generated by a dual PZT mounted on a composite plate. This estimation is confronted to experimental measurements obtained with a laser Doppler vibrometer (for the A mode only). Due to the limited prediction capabilities of the analytical model, a numerical model based on finite element simulations (FEM) in SDTools is introduced. First results indicate good agreement between simulation and measurements. In future studies, the best sensor/actuator combination, the maximum distance between transducers as well as the frequencies to be used for pitch-catch and pulse-echo configurations in a context of delamination detection for SHM application will be determined.
On Performance Limits In Estimating Thickness of a Plate-Like Structure from a Full-Field Single-Tone Response Lamb Wave Measurement
A challenge in wavenumber analysis in steady-state wave fields, specifically in damage detection, lies in identifying the theoretical sensitivity of the system. A tradeoff exists between frequency/wavenumber resolution and the estimated thickness resolution. The current work presents the results of a calculation of the Cramer-Rao Lower Bound (CRLB) for a 1-D multi-mode waveform in Gaussian noise, using the Fisher Information matrix. We conclude by presenting CRLB values against frequency for varying thicknesses. The CRLB values allow for the determination of one or more ideal experimental excitation frequencies given a nominal thickness.
Online Monitoring of Corrosion Damage using Ultrasonic Guided Wave Tomography
Corrosion of pressure vessels, storage tanks and pipelines is a significant problem in petrochemical and nuclear industries. Detecting and quantifying the wall thickness loss due to the corrosion damage is of growing interest. Conventional ultrasonic thicknessgauging methods are tedious and expensive, especially for inaccessible areas. Guided wave tomography offers good potential to monitor the remnant thicknesses of corrosion patches without accessing all points on the surface. It uses the dispersion characteristics of guided waves, and reconstructs the thickness map by the inversion of ultrasonic signals captured by a transducer array around the inspection area. This work applies a novel guided wave tomography method based on full waveform inversion (FWI) for the corrosion mapping. It uses a numerical forward model to predict the scattering of guided wave through corrosion defects, and an iterative inverse model to reconstruct the corrosion profile. At each iteration, numerical modeling is performed with the aim of least-squared minimization of the misfit between the modeled and the observed data. The FWI algorithm allows higher order diffraction and scattering to be considered in its numerical solver, thus can provide accurate inversion results. Numerical simulation and experiment have been carried out to validate the algorithm, showing excellent agreements. A guided wave tomography system is then designed to monitor different stages of forced electrochemical corrosion. The reconstructed thickness map match perfectly with the prediction using the Faradays Law, as well as the measurements from a laser profilemeter.
Measurement of Thickness of Wall-Thinned Plate using Acoustic Wavenumber Spectroscopy and Spatial Local Wavenumber Filtering
The surface of a structure can generate cracks or wall-thinning, due to corrosion. This can eventually lead to the fracture of the structure, which can trigger enormous fatality and property loss. Thereby, a laser imaging technology on such structures as thin plate structure, or piping which thickness is relatively thin in comparison to the area, has been steadily studied for the past 10 years. The most typical among the laser imaging technology is the pulse laser imaging. By using the same, a new technology for inspecting and imaging a desired area within a relatively short period of time was developed, so as to scan various structures including the thin-plate structure and piping. However, this method builds images by measuring waves reflected from defects, and have a time delay of a few milliseconds at each scanning point. Moreover, complexity of the systems is so high due to additional components such as laser focusing parts. This paper proposes laser imaging method with increased scanning speed based on excitation and measurement of standing waves in structures. Wavenumber of standing waves changes at sections with geometrical discontinuity such as thickness. It is shown that defects in a structure can be visualized by generating standing waves with single frequency and scanning the waves at each point by the laser scanning system suggested in this work. The proposed technique is validated by a wall-thinned plate that has a linear thickness variation
Rail Crack Monitoring using Fiber Optic Based Ultrasonic Guided Wave Detection Technology
Rail health conditions are among the top concerns in the area of train safety. In this study, a fiber optic monitoring system is developed to achieve ultrasonic guided wave based rail crack detection. Although fiber Bragg grating (FBG) sensor is a wellknown suitable candidate for long-distance monitoring of rail, the sampling speed of commercially available optic spectrum analyzers limits their application to ultrasonic wave detection. A high-speed FBG interferometric interrogation module is developed, which constitutes the rail monitoring system in conjunction with an active wave generation module and a sensing network. To find appropriate excitation frequency and FBG dimension for ultrasonic guided wave generation and reception, dispersion analysis of rail, a waveguide with complex cross-section, is conducted to guide subsequent design of damage detection experiment. The system and the crack detection technique are then implemented on a long full-scale rail segment, by deploying PZT (lead zirconate titanate) actuator and FBG sensor in pitch-catch and pulse-echo configurations. Artificial cracks in different lengths are introduced to the rail. Frequency-domain analysis of the rail responses is used to identify the damageinduced discrimination after direct observation of time-domain signals. Power spectral density analysis of the purified signals, assisted by discrete wavelet filtering, leads to the graphic presentation of rail integrity.
Selective Guided Waves Generation in a Bi-dimensional Waveguide for Damage Detection
SHM systems aim to detect defects in the structure they are attached to. Carefully selected pure guided waves are to be sent in the structure that acts as a waveguide in order to properly discriminate defects. For plates, this process has been developed in the past decades, e.g. using Lamb and SH waves. However, for a waveguide of finite crosssection, unconventional modes exist and particular attention must be paid to select the generated modes in the waveguide. For a same frequency-thickness product, the number of propagating modes in a bar is much larger than in an infinite plate and they may exhibit several displacement nodes over the bar width. In this paper, guided waves in a rectangular cross-section aluminum bar are studied, as representative of guided wave propagation along joint and extrusions. The two-dimensional Semi-Analytical Finite Element (SAFE 2D) method is used to obtain dispersion curves of all possible propagating modes, as well as mode shapes. Bar modes are generated experimentally in an aluminum bar using an ultrasonic multi-element probe, and detected with a Laser Doppler Vibrometer (LDV). Displacements and amplitudes obtained after processing of the measured data are in agreement with the predicted dispersion curves. Amplitude and phase measurements of modes generated by each excited element of the probe provide a transfer matrix, the inversion of which allows finding the excitation signals to simultaneously send to all elements in order to generate a desired pure mode. Finally, experiments are performed to confirm the selective generation, and to demonstrate a damage detection.
Simultaneous Localization and Classification of Acoustic Emission Sources in Plates using a Guided Wave-Based Sparse Reconstruction
This paper presents an approach for localizing and classifying acoustic emission (AE) events in isotropic plate-like structures. A sparse reconstruction method is used to extract the parameters of a modeled AE event that best describe a recorded AE event. Here, the location and A0/S0 amplitude ratio at the source of the AE event are extracted. A classification based on the amplitude ratio is used to determine if an event is out-of-plane (OP) or in-plane (IP). The orientation (OP or IP) may be used to infer the damage mechanism; for instance, impact or crack. By extracting the amplitude ratio at the source, any propagation effects are removed, which yields a more accurate evaluation of the source orientation. This is in contrast to previous studies that measure this parameter at a distance from the source. The approach is validated through experimentally simulated AE events in an aluminum plate. Events are simulated using pencil lead breaks on both the plate surface and the cross-section of the plate edge to generate OP and IP motions. The ability to both localize and classify the two cases of simulated events is demonstrated. Lastly, the approach is shown to be computationally efficient, taking on the order of 1 second to run on a desktop computer.
Spectral Finite Element Method for Studying the Wave Response on using SH modes in Adhesively Bonded Metallic Joints
The importance of adhesively bonded joints in aerospace industry is undebatable. But a defect proof joint is not guaranteed by even the most sophisticated manufacturing techniques. Hence, health monitoring of these joints is of utmost significance. Use of horizontally polarized shear waves for non-destructive testing of these joints is addressed in this work. Wave responses in a metallic plate are obtained using spectral finite element method. In this method, the displacement fields are first calculated in frequency domain using exact interpolating functions and then transformed back to time domain. This ensures very small computational system size. The spectral element developed here predicts the wave response of the fundamental shear mode that propagates non-dispersively and with a constant speed in a metallic plate. The computational time and effort required using this formulation are orders of magnitude smaller as compared to that using conventional finite element method.
Study of Guided Wave Propagation in Water Immersed Samples with Protective Coating
The paper presents a study of related to structural health monitoring (SHM) in the maritime environment. The research is focused on thin-walled structural parts. The used phenomena is the guided elastic wave propagation. This type of waves can be used in or- der to obtain information about structural condition and possibly damaged areas. Guided elastic waves can travel in the medium with relatively low attenuation, therefore they en- able monitoring of relatively large parts of structures. In this particular research we focus on objects that are partially immersed in water. In reported investigation piezoelectric transducer was used to excite guided waves and laser vibrometer was used for sensing of the response. The change of wave behaviour is studied by frequency-wavenumber characteristics. The analysis is made by comparing measurements from a dry sample with those from a sample immersed in water. Moreover, samples with protective coat- ings are investigated. Two types of layers were considered that differ with the thickness. In this way it was able to learn about differences in wave propagation for the coated and uncoated samples, both in immersion and in dry conditions. The study provides guide- line on the application of wave-based SHM method for inspecting maritime structures in which the inspected element can be immersed in water and covered with protective coating
Torsional Waves for Load Monitoring of Cylindrical Waveguides
This paper is aimed to produce a methodology to monitoring changes in the load condition in the support based on the understanding of how the mode T(0,1) interacts with simple supports under varying load conditions. An analytical expression of the phase velocity for the fundamental torsional mode T(0,1) propagating in a medium under mechanical stress is derived based on the acoustoelasticity effect. A test bench is implemented to emulate a failure or a change in the loading support conditions. A scheme pitch-catch is adopted taking advantage of an affordable way to produce torsional waves in situ based on the magnetostriction effect
Transducer Placement Option for Ultrasonic Lamb Wave Structural Health Monitoring (SHM) on Damage Tolerant Aircraft Substructure
In this paper, we review two transducer placement options to locate and quantify damage in primary aircraft structures using ultrasonic Structural Health Monitoring (SHM). The first placement approach concerns a known expected damage location, for example a fatigue crack growth from rivet hole. The location of such a damage can already be predicted by fracture mechanics and therefore the focus of this SHM system design is to determine the damage size. For this approach, we have developed our previous work in finite-element (FE) modelling of a damage tolerant aluminum fuselage by introducing an artificial crack into the structural FE model and assessed its influence on the Lamb wave propagation. Image processing was performed by subtracting the wave propagation image of the damaged from the undamaged structure. A second category of damage occurs at locations that cannot be predicted by fracture mechanics, such as impact damage from hail. This type of damage requires the SHM system to both locate and assess the size of the damage and this is heavily influenced by the positioning of the transducers. Optimal sensor placement (OSP) techniques tend to rely on assessment using the probability of detection (POD) parameter. In this work, we propose an alternative placement method which maximizes the detectability of the transducer coverage area based on the pulse-echo technique without relying on the POD parameter, by determining the fitness function based on sensor coverage area for single and multiple sensors and random damage locations. Results from both these approaches are compared in this paper, with a perspective towards the overall design of SHM systems.
Interrogation of Linear/nonlinear Features of Guided Waves for Characterizing Hypervelocity Impact-induced Pitting Damage in Shielding Structures
Hypervelocity impact (HVI) is a scenario ubiquitous in low Earth orbit, where HVI is typified by the collision between meteoroids and orbital debris and spacecraft with a relative speed greater than 10 km/s. A linear/nonlinear guided-wave-based approach for characterizing HVI-induced damage in a two-layer aluminum shielding structure (comprising inner and outer layers) is developed. After penetrating the outer layer, the generated debris cloud further impacts the inner layer, producing a unique form of damage with multitudinous small-scale pitting. In this study, aluminum spheres are discharged using a two-stage light gas gun, at an impact speed of ~6 km/s, to introduce HVI to the outer shielding layer. Both linear/nonlinear features of guided waves propagating in the inner layer including various nonlinearity sources are investigated using finite element models, corroborated by experiment. With the models, the accumulation of nonlinear second harmonics (nonlinear features) in the case of phase matching is analyzed. Based on the numerical models and experimental discovery, linear/nonlinear indices are developed, via which a detection approach is developed, able to characterize HVI-induced pitting damage. In the approach, the second harmonics (nonlinear feature) show higher sensitivity to pitting damage compared to the fundamental wave (linear feature). Combining a path-based probability imaging algorithm with defined linear/nonlinear indices, this approach can identify HVI-induced damage to the spacecraft precisely and intuitively.
Advances of Bio-inspired In-situ Triboluminescent Optical Fiber Sensor for Damage and Load Monitoring in Multifunctional Composite
The proprietary bio-inspired in-situ triboluminescent optical fiber (ITOF) sensor was developed by researchers at the High-Performance Materials Institute. The sensor has capability for in-situ, distributed and real time damage monitoring in large civil and aerospace structures. Unlike other sensors, the ITOF sensor does not require external light source or power for sensing damage and transmitting the signal. The ITOF sensor consists of polymer optical fiber and triboluminescent (TL) composite coating of manganese-doped zinc sulfide (ZnS:Mn), the material with the highest TL emission capability among all inorganic crystals. The optical fiber is consistently coated with the triboluminescent composite by a continuous dip coating process. Over the years, researchers have utilized ITOF sensors for damage monitoring in composites and concrete structures. The sensor can successfully detect damage such as delamination, crack, adhesive bond failure, and barely visible impact damage (BVID) in composites. A new sensor, ITOFPress, consisting of ITOF sensor, is being developed to provide real-time quasi-distributed load information on wind turbine composites blades. These works utilized ITOF sensor of diameter ranges 1450 ~ 1600 μm. For the first time, a smaller diameter ITOF sensor of 570 μm was embedded into the carbon fiber composites and tested under flexural loading for damage monitoring. The results showed that small-sized ITOF sensors can detect shear failure of the flexural beams. The successful fabrication and integration of smaller ITOF sensors into the composites reduce the possibility of the sensor having a negative impact on the properties of the host material.
Battery State of Charge Estimation using Guided Waves— Numerical Validation and Statistical Analysis
This work presents a novel scalable and field deployable framework for estimating battery state of charge (SoC) based on pitch-catch guided wave propagation using lowprofile built-in piezoelectric transducers. A preliminary study is performed through experiments using piezoelectric disc transducers mounted on commercial lithium-ion (Li-ion) pouch batteries. Similar experiments are carried out on Multifunctional Energy Storage (MES) Composites, which are an energy-storing structural material recently developed by the authors. In this work, special emphasis is given to the numerical validation of the variation in guided wave time of flight (ToF) due to the SoC-induced changes in electrode mechanical properties. The experimental results are accurately captured in the numerical simulation, which provides significant insight on the complex coupling between electrochemistry and mechanics. Moreover, a first-pass statistical analysis is conducted which demonstrates the efficacy of using guided wave data to obtain accurate prediction of battery SoC. The study also reveals a promising opportunity for exploiting the feature-rich multi-path nature of guided wave propagation to further improve SoC prediction accuracy.
CNT Enabled Fabric Sensors for Highly Sensitive and Large-area Monitoring of Polymeric Composites
The next-generation of hierarchical composites needs to have built-in functionality to continually monitor and diagnose their own health states. This paper presents a novel strategy for in-situ monitoring the processing stages of composites by co-braiding CNT-enabled fiber sensors into the reinforcing fiber fabrics. This would tremendously improve present methods that excessively focus on detecting mechanical deformations and cracks. The CNT enabled smart fabrics, fabricated by a cost-effective and scalable method, are highly sensitive to monitor and quantify various events of composite processing including resin infusion, onset of crosslinking, gel time, degree and rate of curing. By varying curing temperature and resin formulation, the clear trends derived from systematic statistics confirm the reliability and accuracy of the method. More importantly, localized processing information of composites can be achieved in real time upon wisely configuring the smart fabrics with a scalable sensor network. In addition, the smart fabrics which are readily and non-invasively integrated into composites can provide lifelong structural health monitoring of the composites, including detection of deformations and cracks.
Displacement-Amplified Piezoelectric Heel Charger to Generate Power On-the-go
Our daily walking activity provides a sustainable and promising power source for instantly charging mobile devices. However, the low-frequency small deflection nature of walking force imposes great challenges for efficient energy harvesting. To address such challenges, this paper presents a solution relying on a heel charger to lever a multilayer-piezoelectric-bimorph to amplify the input force defection and therefore convert the kinetic energy to electricity in a much more efficient manner. The proposed heel charger can be easily integrated into the shoe and the piezoelectric bimorphs are clamped to a pivot at one end and pined on the other. A rigid short lever beam is excited by the walking force and its defection reflected on the long lever beam will be amplified to bend the piezoelectric bimorphs for power generation. Experiments show that, when exposed to an excitation of 2.8 mm at 2.3 Hz, the heel charger with 3 layers of the piezoelectric bimorphs produces the RMS power of 4.0 mW and the peak power of 17.5 mW, in comparison to the RMS power of 2.3 mW and the peak power of 8.2 mW for that with the single layer piezoelectric bimorph. And thus, the heel charger with more layers can potentially produce more power output, which indicates the great potential of this technology for power on-the-go.
Dynamical Behavior of a Metamaterial Beam
Effect of Polymer-based Self-healing Agent on the Gas Permeability of Cement Mortar After Compressive Loading
Concrete is easily to crack due to its low tensile strength. Once harmful substances ingress through cracks, the service life of concrete will be endangered. The self-healing agent with a high healing efficiency and multiple autonomous is prepared based on pyrogallol and polyethylenimine. This self-healing agent is in liquid state, when the solution is exposed to air, there will be a film formed at the liquid/air interface. This film exhibited well regenerative properties which can be used to seal the cracks of concrete to reduce the risk of ingress of aggressive substances. In this paper, the ratio of the two components of the self-healing agent was investigated to achieve an optimization of strength and generation rate of this film. Then cement mortar specimens of Ф100×50 mm are immersed in the self-healing agent under a vacuum environment to let the pores fill with self-healing agent. Thereafter, the discs are subjected to gas permeability test to exam the effects of self-healing agent on the permeability of cement mortar before and after compressive loadings. Results showed that this material can effectively reduce the gas permeability coefficient of cement mortar with micro cracks. Scanning electron microscopy (SEM) results also confirmed the crack barrier effect of this self-healing agent. Through several loading cycles, this self-healing agent also retained a certain self-healing effect in cement matrix, indicating that this self-healing agent appears to be a promising approach to increase concrete durability.
In Situ Printing of Conductive Poly Lactic Acid Strain Sensors Embedded into Additively Manufactured Parts
This paper presents a technique to additively manufacture a strain sensor into 3D printed parts in order to measure internal strains. Carbon filler material embedded into a base thermoplastic produces a filament that can be extruded using the most common additive manufacturing technology, fused deposition modeling, to produce a plastic geometry that has the ability to conduct electricity. Strain sensors produced using this material operate similarly to commercial strain gauges, i.e., as the part undergoes a change in strain, there will be a change in the measured electrical resistance of the sensor. This paper discusses the various parameters explored and calibration efforts to obtain a working strain sensor.
Incorporation of Gold Nanoparticles for Enhanced Additive Manufacturing and 3D Printing Applications of Novel ‘Smart’ Materials
Advanced additive manufacturing (AM) and 3D printing technologies and techniques have the capacity to revolutionize the way composite materials are produced, manufactured, utilized and repaired across a growing number of fields and applications. This research focuses on the design and development of a new category of ‘nanofunctionalized’ 3D printed parts and materials. On the length scale of several micrometers to nanometers, nanomaterials tend to exhibit unique material properties not often seen in the bulk state that can be utilized for a variety of sensing and material state monitoring applications. During initial testing, we have successfully demonstrated the ability to modify precursor filament to obtain nano-functionalized 3D printed parts and materials. By combining the unique optical properties of gold nanoparticles within a polylactic acid polymer host matrix, we herein present an alternative approach for defect detection in 3D printed parts and materials. Changes in absorbance intensity for 3D printed parts containing gold nanoparticles is studied to determine a relationship between void space, representative of material defects or missing print layers, and absorbance intensity. It is shown that as few as 2 missing print layers (0.2 mm thick void) can be detected within a 0.1% difference when compared control samples using the optical response of gold nanoparticles alone. This work aims to use this type of relationship to quantify and assess the structural integrity of 3D parts and materials prior to and throughout their use. This paper demonstrates that by utilizing a ‘bottom-up’ approach to the AM process, with an initial focus on the selection and design of precursor materials to enable 3D printed parts, in-situ material state monitoring for defect detection can be enabled.
Mechanics and Design of Self-healing Materials to Complement SHM
Structural Health Monitoring pursues the goals of making engineered structures safer and more efficient by enabling automated detection of damage. This can provide a warning before catastrophic failure and supports a paradigm shift in design, enabling lighter weight structures by trading design margins and damage tolerance for increased inspection capabilities. Development of self-healing materials, with an innate capability to repair damage, is pursuing the same goals of preventing catastrophic failures and increasing efficiency by offering a paradigm shift from traditional damage tolerance. Self-healing materials generally fall into several categories; autonomic (selfactuating) vs. non-autonomic (requiring external actuation) and crack closing vs. bonding. Crack closing self-healing materials have the capability to restore large scale geometry after fractures occur but are generally non-autonomic. This form of selfhealing material and SHM are complementary because the materials have the ability to mend damage, preventing catastrophic failure, but requires information of the existence and location of damage. SHM provides a means to detect damage and inform and initiate actuation of self-healing. In order to realize the vision of merging these technologies there are multiple hurdles to overcome. Non-autonomic, crack closing self-healing materials generally consist of shape memory wires / fibers embedded within a structural matrix, similar to a composite. Prior work with this form of self-healing has been limited in capability, only able to restore geometry in a free state. Healing has not resulted in a closing load across the fracture and therefore structures have been unable to withstand external loading during or after healing, nor has ultrasonic inspection of healed structures been informative This paper will present a design process intended to create self-healing structures suited to ultrasonic SHM by pre-straining shape memory wires before fabrication. This results in constrained recovery that produces a closing load across the fracture face in the healed state. The fracture closing load gives the healed structure load carrying capabilities was intended to support ultrasonic wave propagation & inspection. Mechanics based analysis of these structures along with their properties in a “healedâ€ state are compared to experimental results. Finally, continuing challenges to ultrasonic inspection are discussed. Integrating Structural Health Monitoring with Self-healing capabilities will improve understanding and capabilities in each field individually while building toward their mutual goals of improving safety and increasing the efficiency of engineered structures.
Multifunctional Mechanoluminescent Composites for Autonomous Impact Detection of Aerospace Structures
In this study, an impact sensor module was devised and integrated in honeycombcored composites to develop multifunctional structural composites capable of autonomously detecting impact occurrences on aerospace structures. The sensor module was devised through integration of a fracto-mechanoluminescent (FML) material, optical fibers, and photodiode. The multifunctional structural composites were fabricated by embedding the sensor module in honeycomb-cored composites. Under impact occurrences, FML material in the sensor module emits light (i.e., sensor signal) to be transferred through the optical fiber and detected by a photodiode. Impact sensing capability was validated by subjecting the multifunctional composites to impact loading using a drop hammer tester with various impact energy. Analysis of test data was performed by factorial design of experiments to acquire regression model for understanding relationship between impact energy and sensor output, which can be used for designing FML materials, sensor modules, and multifunctional composites.
Nano-engineered Functional Composites with Self-sensing Capability for Ultrasonics-based Structural Health Monitoring
The emerging nanotechnology has ushered a new avenue to tailor conventional composite materials. In this study, a new kind of nano-engineered composite material has been developed with a self-sensing capability for guided ultrasonic waves (GUWs)- based structural health monitoring (SHM). In the composites, a graphene-based selfsensing network is designed, developed and “dispersedâ€ in the epoxy matrix of conventional glass fibre-reinforced composites. The nano-structure of the graphene conductive network is optimized, via which the quantum tunnelling effect formed in the network is linked to material elastic deformation when GUWs traverse the composites, via measuring the changes in the conductivity of the dispersed network. Measurement can be implemented at any position of the composites, owing to the “dispersedâ€ sensing capability of the composites, and this renders a possibility to monitor GUW propagation throughout the entire composite structures at any desired position of the structures, while without a need to use any additional sensor externally attached to or internally embedded in the composites. Experimental validation is conducted, in which GUWs that are self-sensed by the composites are compared with the counterpart signals obtained by conventional sensors (piezoelectric wafers), to observe that the developed composites can be responsive to GUWs with frequencies in an ultrasonic regime up to several hundreds of kilohertz, with no prominent discrepancy compared with conventional piezoelectric wafers. This study has spotlighted a new breed of composites with a capacity of self-sensing and self-diagnosis towards online health monitoring of composite materials and structures
On the Dynamic Acquisition of Electrical Signals for Structural Health Monitoring of Carbon Nanotube Doped Composites
In the last decades, the interest on fiber reinforced polymers (FRPs) has increased due to their mechanical properties and weight saving potential. This has led to the development of novel inspection techniques, being the failure modes of composite structures complex to identify. In this regard, carbon nanotubes (CNTs) have been widely used for Structural Health Monitoring (SHM) purposes, thanks to their excellent electrical properties and piezoresistive behavior, which ensure for SHM higher sensitivities than other conventional techniques. The correlation between electrical properties and mechanical behavior on CNT doped nanocomposites is well known, and several analytical and numerical models have been proposed. However, most of these studies considered static and quasi-static load configuration, whereas the dynamic electromechanical behavior still unknown. This work aims to develop a novel technique for dynamic acquisition of electrical signals for SHM using CNTs. For this purpose, impact and vibration analyses on multiscale CNT-doped glass FRP have been performed. Besides, fatigue tests on CFRP lap joints bonded by a CNT-doped adhesive have been carried out. SHM is carried out by means of voltage measurement during the tests. A potentiometer is used to keep the current constant so that voltage variations correspond to resistance changes across the specimen. Dynamic acquisition is carried out using a high frequency acquisition system. The results of impact, vibration and fatigue tests prove the validity and applicability of the acquisition system. The electrical signals have been correlated with the mechanical behavior of the material, providing information on the system dynamic response and, simultaneously, on damage propagation mode
Printed Electronics for Structural Health Monitoring: Automating the Identification of Cohesive Damage Parameters
Structurally integrated sensors are the basis of many structural health monitoring (SHM) systems [1]. Although integrated sensors allow a more compact design, they change the mechanical properties of the original load-bearing structure. Catastrophic failure due to unfavorable diminution of the structure’s mechanical strength and malfunction of sensors due to mechanical overload have to be avoided. Numerical tools, such as the finite element method (FEM), can help to anticipate damage initiation [2]. These simulations can be used not only to support the design of multifunctional structures but also to facilitate the inverse identification of damage relevant material parameters. [3] To describe the mechanical properties of a particular type of SHM sensors, i.e. metalbased printed electronics on composite materials, a cohesive zone model (CZM) is employed [4]. Based on experimentally obtained reference data, ABAQUS was used to determine the full set of cohesive parameters in a semi-automated way. Semi-automated and empirical parametrization approaches strongly depend on the user’s ability to identify the correct parameters. As a result, automation of these approaches may be limited, inefficient or incapable of dealing with changing experimental and thus numerical setups. To overcome this problem, fully automated algorithms which do not replicate the user behavior are desired. Based on existing experimental data, a procedure for the inverse identification of CZM parameters is implemented. A custom PYTHON script triggers ABAQUS calculations with varying CZM parameter configurations. Quality criteria are introduced to compare the numerical results with the experimental data. Using a response surface methodology in MATLAB, optimal parameters are found that produce the best match between simulation and experiment. Descriptions are given how the employed optimization procedures cope or struggle with common issues such as numerical divergence, efficiency and quality of produced parameters. The procedures are then benchmarked against each other, considering speed and applicability.
Reliability Assessment for Nano Engineered Fatigue Crack Sensor.
The present research presents a novel approach for monitoring fatigue crack growth. The sensor itself is a sheet of Carbon Nanotube (CNT) sandwiched between a pair of electrically insulating layers. Any crack growth below the sensor would disrupt the CNT electrical network, therefore increasing the network resistance. Using orthogonal parallel pairs of electrodes, one would be able to not only measure extent of a crack, but also deduce the orientation based on relative changes seen by each pair; a flaw growing towards an electrode would have a small effect, while growing parallel to an electrode pair would offer a significant measured change. Analytical results are presented along with experimental data for calibrated simulated crack growth in addition to actual fatigue crack growth in aluminum specimens. The eventual goal for the program is to couple this sensor with RFID technology to provide a passive and connector-less assessment of fatigue crack growth
Self-Sensing Health of Carbon Composite Pultrusion Strength Members
In applications such as race boat rigging, offshore umbilicals and energy kite tethers which require light weight and high stiffness strength members, there is no better material choice than carbon composite pultrusion. However, designing an optimized termination profile for fatigue driven applications is time consuming and generating stress versus cycle to failure (S-N) curves is expensive. Fortunately, owing to electrical properties inherent in carbon pultrusions, self-sensing techniques can be correlated to microscopic damage to provide useful new metrics on which to base design specifications. This paper presents a new technique for self-sensing health via 4-wire method of carbon composite pultrusion materials in gauge and termination region of a full-scale system. This self-sensing method sheds light into system stress state and permits measurement of damage from crack propagation due to fatigue using robust and widely available tools. These self-sensing signals have clear potential utility for speeding design tasks, defining lifetime ratings and providing real-time system diagnostics of high performance strength members
Sensitivity Improvement of MWCNTs/Epoxy Composite Strain Sensor
We investigated the optimal manufacturing conditions for a multi-walled carbon nanotubes (MWCNTs)/epoxy composite strain sensor in order to improve the sensitivity of the sensor. The specimens were manufactured and the electrical properties of the specimens were compared according to different MWCNT concentrations and dispersion methods. The sensitivity of MWCNTs/epoxy composite strain sensor was investigated quantitatively by measuring resistance changes according to the variation of a constant strain. Scanning electron microscopy (SEM) images confirmed that MWCNTs/epoxy composites with different CNT concentrations have a good homogeneity and dispersion. In addition, we attempted to evaluate the sensing characteristics and compared them with those of a conventional foil strain gauge. A MWCNTs/epoxy composite was attached to an acrylic beam. The response of the MWCNTs/epoxy sensor was fairly good, and the result was nearly identical to that of the strain gauge. In order to verify the performance of the sensor, a Wheatstone bridge circuit and a signal conditioning circuit were used. This current study on the sensitivity of MWCNTs/epoxy composite sensors provides a useful tool for designing smart sensing and multifunctional polymer composites. The concept of using these conductive fillers provides a low-cost and effective way to fabricate piezoresistive polymer nanocomposites for structural health monitoring applications
Strain Sensor Made by Thick-film Resistors on Substrates of Glass Ceramic
Thick film resistors (TFRs) were screen printed and fired on fluorophlogopite glass ceramic (FGC) substrates with low elasticity modulus. The mechanical property of FGC and piezoresistivity of TFRs were studied. Test results show the sensor has favorable performance parameters including a sensitivity of 7.888, linearity of 0.716%, repeatability of 0.764% and hysteresis of 0.982% when its input is in the range from 0-3043μ. The strain sensors made with TFRs on FGC substrate is possible to be used in SHM of civil engineering.
Structural Health Monitoring of Elastomeric Composites using Mechanoluminescence
Lightweighting of automotive and aerospace structures is achieved by eliminating parts of components without a structural or functional role. Towards lightweighting, fibercontaining resin-based composites (CRFP, GFRP, etc.,) and compliant structures are replacing metals, and complicated kinematic assemblies. The design of load-bearing structural elements made from composites with thin cross-sections and compliant mechanisms present significant challenges for CAD/CAE and require sophisticated experimental methods for validation. There is a need for the development of novel methods and techniques for (a) characterization of ageing, (b) crack propagation and (c) real time determination of the onset of failure in a polymer composites andmultimaterial hybrid motion transfer component. We present mechanoluminescent additives, and the resulting composites – mechanoluminescent composites as structural sensors for design optimization, structural health monitoring and multi-material joining. This paper presents a summary of our results over the past four years, and discusses the following - (i) optimization of the volume fraction of ML (ZnS:Cu, Mn) phosphors for intense ML, (ii) correlation of stress vs luminance of ML particles in an epoxy matrix, and (iii) demonstration of the onset of failure in a composite from light output in a mechanoluminescent polymer composite.
Structural Health Monitoring of Multifunctional Fibre Metal Laminates under Mode I Loading
Delamination is one of the most common degradation mechanisms in fibre reinforced polymer (FRP) composites and occurs due to low interlaminar strength. Such defects are difficult to detect and yield often to catastrophic failure of the composite. In this work a new approach for structural health monitoring (SHM) of fibre metal laminates (FML) for structural applications is investigated, which does not require additional sensors. Therefore, FMLs consisting of glass fibre reinforced polymer (GFRP) with different metal plies are manufactured by the resin transfer moulding process. The interlaminar fracture toughness G (Mode I) is determined and the capacitance change between the metal plies during crack propagation is measured insitu. Within one FML configuration, a metal sheet is applied additionally, which undergo a pre-treatment before application, to improve the mechanical adhesion of the metal/matrix interface bond. Metal sheets are conventionally used in FMLs to improve the mechanical performance such as the bearing behaviour. The experimental test results show a high potential detecting defects in FMLs. Delamination, which occurs between the fibre/matrix interface, can be electrically detected using the capacitance measurement. The FML with the surface structured metal sheet demonstrates promising results for designing tough composites for structural applications. The fractography demonstrates a higher interfacial bonding between the metal sheet and matrix compared to the glass fibre/matrix interface. Consequently, the pre-treated metal leads to higher resistance against delamination in comparison with the GFRP layers.
Structural Hybrid Layer for Critical Strain Detection in Composite Parts: A Multifunctional Approach
The rapid expansion of composite structures in different fields such as aerospace and automotive industries has highlighted the need for a new generation of hybrid materials able to overcome the current limitations of traditional laminates. In particular, because of their intrinsic layered structure, composite materials are sensible to impact damage that can generate internal damage that, if not detected, can lead to critical failures over short periods of time. In this context, a multifunctional approach would guarantee an extension of the life of the composite part by intervening simultaneously on two different aspects: first, the weak out-of-plane properties of the laminate need to be enhanced by introducing an additional hybrid phase within the composite structure and second, critical loads and internal damaged areas need to be immediately detected by monitoring the variation over time of specific electrical properties measured on the same embedded hybrid phase. By operating on both fronts (mechanical and nonmechanical) a true novel multifunctional material can be developed, able to resist to harsh environments and guarantee in-situ structural health monitoring features. This work is focused on the development of a smart hybrid composite layer that can be used in traditional manufacturing procedures and it is obtained by embedding an array of metal wires (e.g. copper, steel and Shape Memory Alloys) at specific depths within the structure of a traditional fibres reinforced polymer (FRP). The correct location and specific nature of the wires was evaluated via a previous mechanical tests campaign in order to optimise the enhancement of the impact resistance, while the non-structural properties were tested by monitoring the variation of specific electrical properties (e.g. resistance and impedance) using innovative High Frequency NDT techniques. Results showed that by including the Structural Hybrid Layer within a traditional laminate it is possible to increase the total safety of a structure by enhancing its reliability and at the same time to reduce maintenance costs via in-situ SHM.
UAV Structure Integrated Energy Storage and Management System
This work discusses design of the complete solution of innovative energy storage system designated to Unmanned Aerial Vehicles (UAV’s). First part of a study describes a possibility of battery and Thin Film Photovoltaics integration with composite structure of the aircraft i.e. structure of the wing skin. Mechanical and electrical aspects of the multifunctional material are described. The next section is devoted to the design of the battery management system (BMS). Key issues and challenges related to safety and efficiency of the UAV are thoroughly considered. BMS also provides state of health verification and estimation both of the photovoltaic material and the battery. Subsequently complete solution containing multifunctional material and BMS is the subject of the simulation that contains prediction of the solar power input depending on weather conditions and BMS operation verification. Last part of the paper presents design of the complete energy storage system in the exemplary application. Presented energy storage system contributes to increase of the UAV fuselage storage capacity at the cost of a slight displacement of the mass towards the wing tip. In high efficiency electric UAV and with favorable weather conditions, applied thin film photovoltaics can be sufficient to accommodate enough energy even for overnight mission. High integrity of the system with the structure and design scalability ensures wide spectrum of implementation and further development possibilities.
Numerical Model for Characterization of Multifunctional Energy Storage Composite Cells, Modules, and Systems
Recent work on multifunctional materials has demonstrated that high-strength composites could be integrated with active Li-ion battery material to create high strength and high energy density storage structures that could meet the transportation requirements on mobility and energy storage density. However, the performance and the design of the multifunctional materials require fundamental understanding of the mechanical behavior of the integrated Multifunctional Energy Storage (MES) Composites systems under various loading conditions. Characterization of this new class of multifunctional materials would become very challenging without an adequate simulation model to guide the tests and validate the results. Therefore, this work presents the mechanical simulation and design of the MES Composites system that consists of multiple thin battery layers, polymer reinforcements, and carbon fiber composites, which results significant challenges in simulation and modeling. To tackle these issues, homogenization techniques were adopted to characterize the multi-layer properties of battery material with physics-based constitutive equations combined with non-linear deformation theories to handle the interface between the battery layers. Second, both mechanical and electrical damage and failure modes among battery materials, polymer reinforcements and carbon fiberpolymer interfaces were characterized through appropriate models and experiments. The model of MES Composite has been implemented in a commercial finite element code. A comparison of structural response and failure modes from numerical simulations and experimental tests will be presented in the paper. The simulated strain distribution and its application to Structural Health Monitoring (SHM) on MES Composites will also be discussed. The results of the study showed that the predictions of elastic and damage responses of MES Composites at various loading condition agreed with the test data. With appropriate material parameters determined from experiments, this multi-physics model can be used as a necessary tool to characterize a failure envelop that governs the design of MES Composites under specified electrical and mechanical loads.
A Probabilistic Approach to Improve SHM Scheme
Damage tolerant structures subjected to fatigue loading require inspection at regular intervals for detection and reporting of possible damages such as fatigue cracks. SHM systems integrated into those structures consisting of a sensor network combined with data analysis algorithms can further enhance the effectiveness of monitoring either by automating the inspection process or by adaptively changing the inspection intervals. To implement such a SHM system, a network of ultrasonic guided wave actuators and sensors including the electronics will have to be deployed to or integrated into the structure. Design of such a system should consider the probable location of damages that have to be monitored and the ways to enhance the sensitivity of detecting those damages. When a damage approaches its tolerable size limit, it should be detected with a higher accuracy compared to when the damage is first detected at an earlier stage and smaller size. Several probabilistic factors can alter the nature of damage and hence can affect the damage detection sensitivity as a consequence of factors like the specific sensor network algorithms applied, the type of damage and the zone of inspection. This is one of the major challenges to achieve reliable monitoring of some evolving wide spread fatigue damage using an integrated SHM system. In this paper, a specific example of fatigue crack initiation from the rivet holes in a stiffened structural component has been simulated considering a probabilistic sequence of fatigue loading. A scheme of determining the probability of damage growth up to a specific size is studied considering the progressive changes in the load path and randomness in the loading sequence. Our present effort is to develop a scheme of modeling and analysis that uses information regarding variability in the loading and relates those to the probability of a critical damage. Detailed understanding of these relationships is considered useful in arriving at design specifications for an integrated SHM system for improving the system’s reliability
Bayesian Calibration and Bias Correction for Forward Model-driven SHM
Forward model-driven structural health monitoring (SHM) is an alternative approach to the two main categories of SHM research: model-driven and data-driven processes. It is a methodology whereby a validated numerical model is used, in a forward manner, in order to produce simulated damage state data for machine learning applications. This paper explores the use of Bayesian calibration and bias correction (BCBC) in order to simulate representative predictions of observational data. The technique allows calibration of a numerical model to be performed in a Bayesian scheme whilst accounting for bias that may occur due to simplifications of the underlying physics in the model. This paper demonstrates the application of BCBC, in a forward model-driven framework, for producing representative damage features for different damage extents and shows a comparison with both experimental and non-bias corrected damage features
Big Data Analytics in Structural Diagnosis and Prognosis
This paper investigates the use of big data analytics in uncertainty quantification, and applies the proposed framework to structural diagnosis and prognosis. As smart sensor technology is making progress and low cost online monitoring is becoming increasingly possible, large quantities of data can be acquired during the monitoring, thus exceeding the capacity of traditional data analytics techniques. We explore the MapReduce technique to parallelize the data analytics and efficiently handle the high volume, velocity and variety of sensor data. Next, both forward and inverse problems in uncertainty quantification are investigated with this efficient computational approach. We use Bayesian methods for the inverse problem of diagnosis, and parallelize the numerical integration techniques such as Markov chain Monte Carlo simulation and particle filter. To predict damage growth and the structures remaining useful life (forward problem), Monte Carlo simulation is used to propagate the uncertainties (both aleatory and epistemic) to the future state. MapReduce is again applied to drive the parallelization of multiple FEA runs, thus greatly saving the computational cost. The proposed techniques are illustrated for the diagnosis and prognosis of alkali-silica reaction in a concrete structure
Bond Graph Based Bayesian Network (BGBN) for Damage Diagnosis in Structural Health Monitoring
On one hand, Bond Graph (BG) is a graphical language allowing the representation of energy transfers within complex dynamic systems and appreciated for its causal modeling properties in different dynamic systems for damage detection and isolation. However, it requires a prior deterministic knowledge of the characteristics of the system components. On the other hand, Artificial Intelligence (AI) can deal with uncertain knowledge and incomplete information by taking the advantage of probability reasoning. Such reasoning is performed with the aid of Bayesian Network (BN) in which the variables of the system form a causality network based on Conditional Probability Distribution (CPD). Nevertheless, the accuracy of the BN highly depends on the prior knowledge of network ordering. We present a hybrid model based on bond graph and Bayesian network—Bond Graph Bayesian Network (BGBN)—for damage diagnosis in the context of Structural Health Monitoring (SHM). Here, our focus is to embed a BG model in a BN model, that enables us to benefit from the causal properties of both models: (i) BG as a skeleton of the BN to overcome the variable ordering issue and (ii) BN as a tool dealing with variable uncertainties by virtue of historical data, expert knowledge, and other monitoring-induced data. To this end, one-story shear-building is considered and transformed into BGBN. By introducing corresponding CPDs based on expert opinion, the network is activated. The effectiveness of the network is investigated by applying different structural measurements as evidences in order to find and rank the most probable causes. It is seen that by utilizing BG, not only the task of complete determination of the system variables is carried out, but also the diagnosis of damaged components from BN is enhanced.
Damage Classification based on Machine Learning Applications for an Un-manned Aerial Vehicle
Unmanned aerial vehicles (UAVs) are well-known by its advantages in several applications such as surveillance and monitoring for instance in agricultural applications or fire control among others. These missions can be associated to the robotics area due to the smart applications and tasks that can be performed by these systems autonomously. Although its designs and developments are in most of the cases joined to the applications, currently it is possible to design or acquire a UAV for specific applications by defining features about the task to perform. One of the problems with the use of UAV is attached to the variations of the operational conditions which can produce some damages during operation, landing and de-landing tasks. Since several damages can affect the structural state of these vehicles, the use of a Structural Health Monitoring system is a necessity to provide an automatic monitoring system. This work includes a description of a preliminary damage detection and classification system for a UAV. The system includes the description of the data analysis from a piezoelectric sensor network with independent component analysis and machine learning approaches. Some tests are available to validate the system with data from a wing of the UAV called VANT Solvendus from the Fundación Universitaria Los Libertadores. Tests and the application of the methodology for detecting and classifying damage are performed to a part of the UAV wing skin and results show the advantage of the methodology
Damage Localization Methodology using Pattern Recognition and Machine Learning Approaches
Structural health monitoring (SHM) is regarded as a very important field of development in engineering applications. Some benefits of the applications of a SHM system include the improvement of the security of the structure and the reduction of time and cost of its maintenance. Therefore, the service life of the structures can be lengthened. Besides, the implementation of a SHM system provides real time information about the current state of a structure, not only in civil applications but also in military applications, like aircraft, buildings, wind turbines and others. Gathering the maximum information about the current state of the structure to be diagnosed allows a better decision both in the preventive maintenance or the corrective maintenance. The information that can be gathered is the presence or not of damage, the size of damage or even the extent of damage. However, damage localization offers some very interesting possibilities since it allows to accurately determine where the damage is located thus saving time and effort. This work presents a damage detection and localization methodology by means of machine learning approaches which works by analyzing data from a piezoelectric sensor network attached to the structure under test. Validation of the methodology is performed by using an aluminum rectangular pipe with several damage positions
Data Fusion of Multiple Sensing Techniques
This paper investigates the use of data fusion from multiple sensing methods for structural damage diagnosis. In order to monitor structures effectively, multiple sensing techniques are preferable compared to a single sensing technique. However, one of the challenges in information fusion is heterogeneity of the data obtained from different types of sensing techniques. The heterogeneity is caused by different techniques producing data in different formats, such as time series signals, images, and point measurements. This paper investigates a Bayesian network approach to handle this challenge. Structural health monitoring of concrete structures is used to illustrate the data fusion methodology. The objective is to diagnose the presence of alkali-silica reaction (ASR) in a concrete slab. Three types of techniques are used: infrared thermography (IR), nonlinear impact resonance acoustic spectroscopy (NIRAS), and vibro-acoustic modulation (VAM). Vibration signals are generated from NIRAS and VAM, while IR generates thermal images over time. NIRAS and VAM can qualitatively identify the existence of ASR, whereas IR can locate and quantify the area of ASR damage. The Bayesian network is used to fuse the information from multiple techniques and diagnose the ASR (presence, location and extent), and also to quantify the uncertainty in the diagnosis result
Delamination Detection in Laminated Composite with Uncertainty Due to Material Degradation in Damaged Region
Ultrasonic guided wave sensitivity to damage severity in a laminated composite due to impact induced matrix crack is investigated. Ultrasonic guided wave has certain advantages such as long-range propagation without much attenuation, ability to bring damage induced signature from buried location. Those advantages can be exploited to monitor larger area of a complex structure with a few numbers of transducers. Due to the impact load on a composite laminate, matrix cracks can develop and the degradation in the material stiffness develops in the transverse direction with reference to the fiber orientation. Progressive matrix cracking further coalesces and initiate delamination. Ultrasonic guided wave propagation in a laminated composite with matrix crack is studied using numerical simulation. For fast simulation, a time domain spectral finite element (TSFE) scheme is developed, where a higher-order spectral family of interpolation functions is employed to model the wave propagation in a laminated composite. Development of matrix cracks without and with delamination at the adjacent interface is modeled and the sensitivity of the ultrasonic wave is determined from the simulated sensor signal obtained due to scattering from the damage region. Power spectral density of the signal is represented statistically to develop further insight to the problem of estimating the damage severity. Confirming delamination initiation with received signal in the laminate composite structures is of great interest. The nature of uncertainty in the received signal due to variation in the matrix crack regions is studied using statistical approach
Expected Utility Theory For Monitoring-Based Decision Support System
In theory, we all recognize that Structural Health Monitoring (SHM) should help infrastructure owners to make decisions on the management of their assets. In practice, we often observe owners who are very sceptical of the benefit of SHM and act based on their experience, disregarding the actions suggested by SHM. In this contribution, we wish to fill this gap by outlining a formal approach to rational decisions based on monitoring information. SHM and decision are two separate processes, occurring one downstream of the other: whereas SHM allows assessing the state of a structure based on monitoring data, decision provides the optimal action, based on the structural state assessed and the possible consequences of any individual choice. The first process is rationally tackled by using Bayesian inference, the second by using the axioms of expected utility theory (EUT). We illustrate this approach on a real-life case study, the Colle Isarco viaduct, among the longest spans in the European Alpine region. The decision support system (DSS) developed interprets the data coming from a robotized topographic monitoring system, infers information on the state of the structure, and suggests the optimal management policy after a possible damaging event.
Kernel Dependence Analysis for Structural Health Monitoring with High-dimensional, Small Size Datasets
Health monitoring functionality is a key feature of smart and sustainable structures. The statistical inference problem for this functionality usually boils down to novelty detection which aims to distinguishing the intact and damaged states of structures. An important challenge in this unsupervised inference problem is the characterization of dependencies between structural responses at various locations. Failure in considering this phenomenon usually results in high false positive (FP) rates and hence, inaccurate damage localization. The existing methods, such as graphical models or conditional classifiers, mainly use density estimation for considering the dependencies of random variables (RV) in structural health monitoring (SHM); and as a result, these methods usually suffer from the curse of dimensionality which makes them unable to handle high-dimensional problems. In this study, we propose a new approach for novelty detection that uses kernel dependence techniques, instead of density estimations methods, for considering statistical dependencies of RVs. Thus, the proposed method can handle arbitrarily high-dimensional problems with no simplifying assumptions. The performance of the algorithm was tested by experimental data in a SHM application problem. The results show that the damage localization accuracy can be significantly improved by the proposed technique compared to its peer methods
Multi-Task Sparse Bayesian Learning For Model Updating In Structural Health Monitoring
In this paper, we focus on a multi-task sparse Bayesian learning (SBL) theory that simultaneously utilizes multiple measurement vectors that are marked by a similar sparseness profile. Joint learning for sparse representations of multiple tasks has been mostly overlooked, although it is a useful tool for exploiting data redundancy by modeling the statistical relationships within groups of measurements. We first present a hierarchical Bayesian model and Bayesian inference framework for the multi-task SBL algorithm. Then we investigate the application of the multi-task SBL for model updating in structural health monitoring. The results verify that exploiting common information among multiple related tasks leads to better performance of the Bayesian inversion
Non-linear Damage Classification based on Machine Learning and Damage Indices
The use of guided wave-based approaches presents some advantages in the structural inspection and damage identification processes. It is driven by the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects, however, its use in Structural Health Monitoring requires the development of efficient SHM methodologies to analyse and provide confident results. To do that, signal processing techniques for the correct interpretation of the complex ultrasonic waves are a need. In this sense, it is necessary to still work on the continuous search of methodologies for performing each one of the steps in the damage identification. As contribution, this paper presents a damage classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network. The document presents the description of the methodology including a description of the data reduction and the use of non-linear analysis of the information with hierarchical non-linear principal component analysis and some non-linear damage indices. The methodology is preliminary evaluated with a CFRP sandwich structure with some damages on the multi-layered composite sandwich structure which were intentionally produced to simulate different damage mechanisms, i.e. delamination and cracking of the skin. Finally, results are presented and discussed to remark the advantages and disadvantages of this methodology.
On the Choice of Optimisation Scheme for Gaussian Process Hyperparameters in SHM Problems
Within Structural Health Monitoring a number of problems exist which require estimation and prediction of quantities such as load, strain, or acceleration. This can be achieved through Gaussian Process (GP) regression models, which provide a powerful framework for Bayesian machine learning. The ability of GP models to automatically return confidence intervals and not needing to specify a topology, as with Artificial Neural Networks, makes them an appealing choice in engineering problems where quantifying uncertainty is important to the model application. Although GP models are nonparametric, at the heart of the model is the optimisation of the marginal likelihood of the data with respect to the hyperparameters which govern the covariance function. Within many software packages that implement GP models, this optimisation is achieved via a conjugate gradient descent, since the derivatives of the marginal likelihood are cheap to compute once the function value is obtained. This paper investigates the effect of choice of hyperparameter optimisation scheme on the quality of fit of the model and the training time — to determine if a conjugate gradient optimisation is sufficient to identify the covariance function hyperparameters. A number of optimisation schemes are tested on synthetic data, and also on data from in-flight monitoring of aircraft dynamics. Specifically the problem of strain prediction for fatigue monitoring of a Tucano TMk1 trainer aircraft is considered. The effect of input data dimensionality on the ability of the optimisation schemes to find hyperparameters which suitably describe the data is also considered. This paper aims to begin a discussion into when the cost of more complex optimisation schemes is justified for Gaussian process hyperparameter optimisation.
Vibration-based Damage Localization and Estimation Via the Stochastic Functional Model Based Method (FMBM)— An Overview
The Functional Model Based Method is a unique vibration–based approach to damage localization and magnitude estimation in structures under normal operation, capable of operating on naturally arising random vibration signals without human intervention. It employs an innovative data–based stochastic Functional Model representing the partial dynamics under various damage scenarios, estimation techniques, and statistical hypothesis testing. Unlike alternative vibration methods employing data–based models but treat the localization and magnitude estimation approximately, as classification problems, it handles them properly as estimation problems. Among its remarkable properties is its ability to operate with a low number of vibration response sensors and limited/low frequency bandwidth; characteristics important in many applications. In this article an overview of the method and its principles are presented, along with three validation case studies.
Resilience to Extreme Events: A Bayesian Nonparametric Approach
Decision making about the management of infrastructure systems is based on models of cost, degradation, effectiveness of maintenance actions. These models can be informed by data collected in the past, and progressively updated as more data becomes available. While parametric models are suitable when an appropriate statistical form is assumed, non-parametric models provide a flexible alternative, which does not impose a specific pre-selected form. This paper investigates the use of non-parametric modeling in the emergency management of infrastructure systems after extreme events. We consider a Markov management process, and we introduce a nonparametric approach called infinite Markov decision process (iMDP), to perform adaptive control. The approach is made up by two parts. In the learning phase, we use a Bayesian iterative approach to update the model uncertainty, and a sampling approach to represent that posterior distribution. In the planning part, we adopt a myopic optimization scheme based on dynamic programming. The approach has the potential of enhancing system resilience, by the interaction between adaptive learning and planning under uncertainty. We apply the method to a small-scale numerical benchmark, and discuss its performance in that setting. This paper illustrates a preliminary investigation, and the performance analysis in more complex settings, or under alternative conditions, requires additional work.
Verification of Fatigue Path Probability Integration (FPPI) for Fatigue Risk Management with Multiple Maintenance Options
A fatigue path probability integration (FPPI) algorithm was developed for fatigue risk management using nondestructive inspection (NDI) or in situ NDI for multiple maintenance scenarios. FPPI is an efficient probabilistic method which is a probabilistic framework using a baseline crack growth histories repeatedly for multiple maintenance plans under various uncertainties such as variability in material properties, initial flaw size, random processes of the fight loading, inspection reliability and inspection dependency for complex repair scenarios for high computational accuracy and efficiency. In this study, Monte Carlo simulations (MCS) with several billions of flight simulation were conducted to verify and demonstrate the FPPI algorithm for flight life plans with multiple maintenance options and variable inspection intervals under various flight conditions subject to various uncertainties. The study also demonstrated that FPPI is several orders of magnitude more efficient than traditional MCS for equivalent accuracy
A Deep Learning Approach for Single-sensor Acoustic Emission Source Localization in Plate-like Structures
This paper presents a deep-learning approach to localize acoustic emission (AE) sources in plate-like structures using a single sensor. This approach implicitly uses the edge reflections of Lamb waves and their dispersive characteristics to localize their sources. Specifically, the deep learning model directly learns such reflection and dispersive characteristics from the continues wavelet transform of the AE signals. Therefore, there is no need to use the traditional AE features, such as arrival time, rise time, and amplitude. In this paper, the deep learning architecture consists of a stack of two autoencoder layers and a softmax layer. To train and test the performance of the proposed deep learning approach, standard pencil lead break tests were performed next to the rivet connection of an aluminum plate with its stiffener. The result shows that this approach can accurately identify the rivet at which simulated fatigue cracks (with pencil lead break tests) generate AE waves
A Mechanics-informed Method for Real-time Acoustic Emission Source Classification During Fatigue Loading of Composite Structures
This paper presents a new approach aiming to improve the classification method of Acoustic Emission activity with a material mechanics perspective and to provide the means for real-time classification. This mechanics-informed approach consists of using the instantaneous fatigue cycle phase as hit labels. Two different types of materials were tested under Tension-Tension fatigue loading at R=0.1. The main goal was to trigger matrix cracking as the dominant failure mechanism and measure the stiffness degradation. In order to validate the approach, the stiffness degradation was measured using Digital Image Correlation. From the fatigue cycle phase labelling of hits, at least two distinct clusters were observed: first, the hits occurring mainly in the loading phase of the fatigue cycle, whichs activity correlates with the rate of stiffness degradation in the test specimens; and second, the hits occurring in the lower part the unloading phase of the fatigue cycle.
A Structural Health Monitoring Technique for the Reconstruction of Impact Forces in Aerospace Components
Despite composite materials are widely used in aerospace components, they are characterized by low-impact resistance and, therefore, subject to barely visible damage. This can be due to a number of sources such as hailstones, tools drop, runaway debris and bird strikes. Hence, the real time knowledge of low-velocity impact magnitude on aeronautical composite structures is certainly one of the most demanded goal for structural health monitoring (SHM). This article proposes a novel ultrasonic SHM method for the reconstruction of the impact force history on composite components. The research work is based on an inverse problem applied to composite samples of unknown mechanical properties, whose global characteristics are obtained through the knowledge of the structural transfer function measured by a sparse array of surface-bonded piezoelectric sensors. Hierarchical interpolation methods are then used to reconstruct the magnitude of the impact force over the entire structural domain. Since the proposed methodology only requires the knowledge of the material response at discrete locations, it does overcome the limitation of current impact force reconstruction techniques, which require the use of either analytical models based on classical plate theory or numerical finite element simulations. This technique is experimentally validated on a composite panel with complex geometries by considering low-velocity impacts at multiple locations and different levels of magnitude.
A Robust-to-parameterization Clustering Fusion Approach for Monitoring Damage Onsets and Growth using Acoustic Emission
A methodology is presented for acoustic emission (AE) data processing and interpretation, well suited for material characterization of laboratory specimens and in-service structural health monitoring. The methodology relies on the AE streaming in which relevant transients are detected using wavelet-based wave picking, feature vector cleansing with a Mahalanobis-based procedure and a new approach for pattern recognition relying on clustering fusion. The proposed clustering fusion method emphasizes damage kinetics related to the continuous-time definition of AE signals, quantifies uncertainty on clusters (AE sources) evolution, and evaluates the robustness of the results with respect to the change in the parameterization using mutual information. Illustrations concern the early detection and monitoring damage onsets and evolution in a thermoplastic thermostable composite tubular bandage for high-speed rotating engine.
CFRP-Concrete Debonding Detection and Monitoring using Low Cost Piezoceramic Sensors
The objective of this study is to investigate the applicability of low cost piezoceramic sensors for CFRP-concrete debonding detection and monitoring. Two types of applications were proposed, i.e., existing debonding detection and in-situ debonding process monitoring. A concrete beam reinforced with CFRP sheet was loaded through the 3 points bending test till failure to induce debonding between CFRP sheet and the concrete substrate, and piezoceramic sensors were used to detect the existing bebonding by analyzing the receiving ultrasonic waves. In addition, the debonding detection results were further compared with and verified by the air-coupled non-contact testing and vision-based strain testing results. For in-situ debonding monitoring, 10 piezoceramic sensors were used as an array to track the wave transmission changes during the loading process of a CFRP reinforced concrete beam, and the debonding development process was successfully monitored. The test results show that the low cost piezoceramic sensors are very effective to generate and receive ultrasonic waves, and are capable of detecting the existing debonding and monitoring the in-situ debonding process as well.
Characterization of Distributed Cracks in Concrete using Randomly Scattered Wavefield Extraction
This paper presents a fully contactless air-coupled ultrasonic scanning approach to characterize distributed cracks in concrete. The approach enables characterization of near-surface distributed cracks in concrete, for example that caused by alkali-silica reaction or freezing and thawing action. A series of numerical simulations are performed to understand surface wave scattering phenomenon caused by distributed cracks in concrete. The numerical simulation results reveal that incident surface waves undergo complicated random scattering within the cracked region. As a result of the random scattering, local wavefields having a broad wavenumber spectrum manifest. A frequency-wavenumber (f-k) domain signal filtering approach is applied to extract the randomly scattered wavefields set up by distributed cracks. The feasibility of the proposed approach is established using a series of experiments on laboratory-scale concrete specimens including simulated distributed cracks. The experimental wavefield data obtained by fully contactless ultrasonic scanning measurements is analyzed using the f-k domain signal filtering approach. The experimental results demonstrate that distributed cracks in concrete can be successfully characterized and further that the the extracted scattered wavefield energy is closely related to the crack density
Clustering-based Crack Growth Characterisation using Synchronised Vibration and Acoustic Emission Measurements
Monitoring and predicting fatigue crack growth is a critical part of the prognosis step of Structural Health Monitoring (SHM). It is also arguably the most difficult step owing to the uncertainties associated with future loading conditions and fatigue processes. For this study, the authors focus on the use of machine learning, and more specifically clustering, to find groups of features in measured data that are able to predict the current state of fatigue life. The data, in this case, is a combination of vibration and Acoustic Emission (AE) measurements. Individually, both of these measuring techniques have been previously used to characterise crack growth. Here, it is shown that features that are particularly useful for clustering, and potentially other machine learning techniques can be extracted if AE data is synchronised to the main excitation frequency during harmonic loading.
Compressive Sensing for Direct Time of Flight Estimation in Ultrasound-based NDT
This paper presents an approach for estimation of ultrasonic time-of-flight (TOF) within a Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) context. The presented method leverages recent advances in the field of Compressive Sensing (CS), which makes use of sparsity in a transform domain of a signal in order to reduce the number of samples required to store it. CS achieves this through a two key ideas: random matrix projections, and l1-penalised linear regression. In this case, sparsity arises from the observation that in a pulse-echo ultrasound test, the number of echoes is relatively small compared to the number of measurement points in a waveform. This sparsity is evident in the autocorrelation of ultrasound waveforms. A method is suggested in this paper for building suitable basis functions, based on Hankel matrices, which transform a signal into its autocorrelation domain. It is shown how this can be combined with standard CS techniques in order to achieve a very low error in TOF estimates with up to one-tenth of the original ultrasound samples.
Damage Classification in Aeronautic Structures using Guided Waves
A composite aircraft door surround structure with an integrated SHM network has been manufactured. The structure has been impacted creating realistic damages such as skin delaminations and stringer debondings. The guided wave signal has been acquired using the SHM-network, allowing the subsequent damage identification. The presented work focuses on the classification of different types of damage using a datadriven approach. The method focuses on feature selection and a Support Vector Machine (SVM) algorithm for damage classification. The results show a SVM algorithm capable of distinguishing between three damage types
Degradation Entropy: An Acoustic Emission Based Approach to Structural Health Assessment
This paper reports experimental estimation of the relative entropy associated with acoustic emission obtained in the course of the fatigue degradation of the Al 7075-T6 specimens. A non-parametric approach is employed to quantify the relative entropy of the acoustic emission signals. The trend in the evolution of the cumulative relative entropy corresponds to of the cumulative damage in the tested specimens. We also examine the hypothesis of a constant cumulative acoustic emission entropy at the instant of crack initiation.
Delta-T Acoustic Source Localization for Plate with Holes
Acoustic Emission is a powerful tool for real-time condition monitoring of structures, based on the principle of propagating elastic waves resulting from sudden stress variations. It is a passive Non-Destructive Testing technique that is capable of detecting and locating crack growth and impact over larger areas unlike traditional methods. Time-of-arrival source location technique, which triangulates the signal arrival time of the propagating waves, assumes straight wave propagation path and fails when applied to complex geometrical profiles. Delta-T method, an experimental gridbased methodology, has reportedly been capable of overcoming the limitation with successful source location on composites and metal plates with circular holes. Since square holes have edges that form perpendicular barriers to the direction of incident waves, they result in longer wave propagation paths and have a higher propensity of blinding sensors from the propagating waves. This study extends the evaluation of Delta-T method to plate with square holes. Maps of difference in arrival times between sensor pairs are obtained for three square stainless steel plates with no hole, a circular hole and a square hole respectively. The validation points are then located by overlapping contour lines obtained from the respective Delta T maps. Experimental results indicate that Delta-T method provides an improvement in accuracy of 11 times over TOA for the source location estimation on plate with square hole and gives 5 times more precise estimation across all 3 metal plates with different geometries.
Enhancing the Robustness of Nonlinear Ultrasonic Testing by Implementing 1D Phononic Crystals
Nonlinear ultrasonic method is based on detecting higher order harmonic signals and relating their change with respect to the fundamental mode to microstructural features in a material. When ultrasonic wave propagates in a material, the waveform gets modified when it confronts microstructural features. Higher harmonics generated by the interaction of fundamental mode with microstructural features are measured to calculate the ultrasonic nonlinearity parameter known as β . However, typically, materials nonlinearity due to microstructural features is very weak and nonlinearity in the experimental instruments may produce significant error. This paper aims to enhance the robustness of the nonlinear ultrasonic method by blocking the nonlinearity induced by measurement process. Phononic crystals are periodic composite materials that generate a bandgap to restrict propagation of selected range of frequencies. In this study periodic phononic crystals and the nonlinear ultrasonic testing are integrated to block the higher harmonics generated in the experimental process. One dimensional phononic crystal, called superlattice, is numerically modeled using COMSOL Multiphysics software to block the frequency range of 4 MHz to 6 MHz. The model is tuned to block the higher harmonics while it allows the fundamental frequency of 2 MHz to pass. By placing the superlattice between the transmitter and the specimen, the higher harmonics generated in the experimental process are extensively decreased. This method has been experimentally implemented to detect the dislocation density changes in aluminum 1100 specimens. The results show that applying the superlattice into the nonlinear ultrasonic testing can improve the sensitivity of measuring microstructural features.
Full-field Ultrasonic Data Analysis Based on Statistical Covariance Method
Laser ultrasonic techniques (LUTs) are becoming widely used for damage detection based on full-field ultrasonic data. In general, LUTs provide threedimensional (3-D) ultrasonic signals from which significant informative features may be extracted about the health condition of a structure. In this paper, statistical convariance method is proposed to investigate 3-D ultrasonic features. A laser ultrasonic interrogation method based on a Q-switched laser scanning system was used to interrogate 3-D ultrasonic signals in to a 2 mm aluminum plate. The ultrasonic signals in three-dimensional (3-D) space were generated, indexed by spatial N (xdirection), spatial M (y-direction), and temporal K (t-direction) samples, respectively. Then, two sets of covariance matrices were obtained based on the vector variables in x-direction and y-direction respectively for all k time samples respectively. The covariance imaging and the corresponding variance map were obtained. The wavelength and group velocity of S0 and A0 modes were able estimated from the variance map. The wavelength of the S0 and A0 modes were estimated with the deviation error of 2.3% and 11% respectively. The group velocities of S0 and A0 modes were estimated with the deviation error of 1.8% and 0.9%. The analysis will be further performed to develop group velocity curves for a range of frequency in metal and composite plates.
Identifying the Effect of CFRP Application Type on Behavior of CFRP-Strengthened Beams by Acoustic Emission
Strengthening structures using CFRP (Carbon Fiber Reinforced Polymer) strips is a widely used method because of superior mechanical properties of CFRP. Along with the overall impact of the FRP strengthening, FRP application type is one such important parameter that directly affects the strengthened beam behavior. FRP can be applied in a number of different ways. FRP sheets, either full wraps or strips are the mostly preferred methods for strengthening in recent years. These methods have more or less advantages according to each other. In other words, CFRP application influences the strengthened beam behavior since application type has a direct impact on the failure mechanism. In this study, it was aimed to identify the effect of CFRP application type on behavior of reinforced concrete (RC) beams. For this purpose, CFRP-strengthened RC beams, which were strengthened by different application types such as side-bonded and U-jacketed CFRP sheets, were tested under cyclic loading and monitored by Acoustic Emission (AE) technique. AE data obtained from the tests were processed by parameter and SiGMA analyses. Crack locations, types and orientations were determined and were compared with mechanical results. Thus, failure mechanisms of the specimens were evaluated and effects and discrimination of CFRP application type on the fracture mechanism were revealed.
Impact Induced Delamination Detection by Application of Full Wavefield Analysis
Comprehensive numerical studies of elastic wave interaction with damage of various sizes and severities which resembles impact damage were performed. Full wavefield signal processing allowed to visualize damaged area and quantify its size by the thresholding damage maps. It was shown that the severity of damage can be quantified by relation of damage map energy in relation to Young’s modulus reduction. It enables to make a step towards damage prognosis. Experimental data of propagating waves in carbon/epoxy laminate acquired by a scanning laser vibrometer revealed that the wave attenuation is highly dependent on angle of propagation. Such behavior strongly affects damage visualization algorithms which are based on a wavenumber filtering. It was shown that this effect can be reduced by the proposed weighting method utilizing propagating elastic wave energy spread. It was also shown that the damage caused by the low impact energy is more difficult to detect and quantify than a delamination simulated by the Teflon insert.
In-situ NDE of Composite Repair Patch and Thick Panel with Substructures using Mobile Pulse-Echo Ultrasonic Propagation Imager
Composites are widely used as structural materials due to its advantage for weight to strength ratio compared to metallic structures in aircrafts. Thus, their usage is increasing in aircraft industry and the technique of composite manufacturing has been rapidly developing. However, detection of defects in composite structure is challenging compared to that in metallic structure. Based on this, various structural health monitoring and nondestructive inspection techniques have been researched to detect the defect in composite structure. Among the techniques, in-situ nondestructive evaluation (NDE) is increasingly used to avoid time-consuming work such as aircraft disassembly for inspection. Recently, laser ultrasonic generator is widely used because it generates broadband ultrasound of diverse modes by thermoelastic mechanism and it works as non-contact excitation. In addition, non-contact sensing laser for measuring ultrasound is drawing attention because it has a benefit of non-contact sensing in inaccessible area. In this study, fully non-contact mobile pulse-echo ultrasonic propagation imager (PE UPI) based on laser ultrasound is introduced as an in-situ NDE tool. The mobile PE UPI is configured with scanning laser head, two-axis translation scanner and UPI controller. The scanning laser head including generation and sensing lasers scans with two-axis translation stage to generate and sense pulse-echo through-the-thickness ultrasounds as bulk wave. After completing the scan, the defects are visualized by fullfield pulse-echo ultrasonic wave propagation imaging (FF PE UWPI) technique and variable time window amplitude map (VTWAM). For nondestructive evaluation, the mobile PE UPI inspected composite repair patch and composite thick panel with substructures. As a result, the defects in the composite repair patch and composite panel with substructures were visualized by FF PE UWPI video and VTWAM
Nonlinear Ultrasonic Reciprocal Method for Damage Localisation
Nonlinear ultrasonic techniques are based on the measurement of nonlinear elastic effects in the material, such as the magnitude of second harmonic response, in order to reveal the presence of surface and sub-surface flaws. These methods have shown to be more sensitive to damage-related structural alterations than any other ultrasonic technique, which typically relies on the investigation of linear material parameters such as wave speed, damping and wave scattering. This paper presents a novel nonlinear ultrasonic technique, here named as nonlinear ultrasonic multi-path reciprocal method, for the localization of material damage based on the measurement of nonlinear elastic effects using a spatially distributed array of surface mounted ultrasonic transducers. In the proposed methodology, the reciprocal nonlinear material response is measured between each pair of sensors acting both as transmitter and receiver. The resulting damage location is mapped to tangent circumferences centred at the transmitter location for each pair, and all contributions (multi-path) from all pairs are summed to achieve the final defect location. Both the second order nonlinear parameter and the bispectrum coefficient are used as a nonlinear ultrasonic signature due to signal noise effects hiding the nonlinear harmonic response. The proposed methodology is validated on a damage composite plate. Unlike traditional ultrasonic methods, this technique does not require any knowledge of the mechanical properties and layup, nor any baseline with the undamaged component.
Model-driven, Wavefield Baseline Subtraction for Damage Visualization using Dictionary Learning
Wavefield acquisition is a powerful visualization tool for studying wave propagation. Yet, without a baseline, damage visualization is a challenge because the damage wavefield is usually orders of magnitude weaker than the incident waves. Researchers have created several baseline-free approaches for suppressing incident waves, but these methods often rely on simple assumptions. We address this challenge with a dictionary learning framework that uses simulation data to guide the suppression of incident waves. Dictionary learning produces new baselines with geometric features similar to the simulations as well as velocity and frequency response features similar to the experimental data. We show that our framework can visualize reflections from a circular 2 mm diameter half thickness hole in a 10 cm × 10 cm steel plate without a baseline.
Pushing the Limits of Ultrasonic Imaging of Solids by Wave Mode Beamforming and GPU Processing
This paper proposes a novel imaging approach for damage identification in structural components. Improvements to conventional Synthetic Aperture Focus (SAF) algorithms are introduced by exploiting multiple wave propagation modes (longitudinal and shear) in the bulk of the material. Significant reduction of artifacts is achieved through the application of adaptive weights based on the wave mode structure and the coherent compounding of longitudinal and shear waves. Further improvement can be achieved by introducing various signal processing techniques, such as baseline subtraction and deconvolution of the point spread function. Finally, utilizing the massively parallel processing structure of a Graphical Processing Unit (GPU) architecture, a considerable increase in speed can be achieved compared to a traditional serial processing technique based on a Central Processing Unit (CPU). A 64-element linear array connected to a data acquisition system was used for experimental validation of the proposed tomographic algorithm steps. Results will be shown from tests performed on rail sections with simulated and natural flaws acquired from the Federal Railroad Administration (FRA) rail defect library. Considerable reduction of side lobes and improvement of image contrast are demonstrated. Future extensions of this work are also investigated, including guided wave inspection in a multi-mode environment and high-speed inspections.
Recent Progress on a Multi-Modal SHM System for Waveguides
We present a structural health monitoring (SHM) system based on the passive method of acoustic emission (AE) and the active methods of electromechanical impedance (EMI) and guided ultrasonic wave (GUW) methods. As all these methods can be deployed with the use of wafer-type piezoelectric transducers bonded or embedded to the structure of interest, this paper describes a unified SHM system where AE, EMI, and GUW are integrated in the same hardware/software unit. We assess the feasibility of this multi-modal monitoring in a large flat aluminum plate with six transducers. AE events are simulated by exciting a tone burst or using the conventional pencil-lead break test and the detected signals are processed with a source localization algorithm to identify the position of the source. For the active sensing, damage is simulated by adding a small mass to the plate: the raw waveforms are processed with a delay-and-sum algorithm to create an image of the plate whereas the electrical admittance of each transducer is analyzed using the statistical index of the root mean square deviation. The results presented in this paper show that the proposed system is robust, mitigates the weaknesses of each technique considered individually, and can be developed further to address the challenges associated with the SHM of complex structure
Structural Displacement, Velocity and Acceleration Measurement System based on Accelerometer and GPS-RTK for Large-scale Civil Structures
The latest displacement and velocity sensors such as LDV and LiDAR have limited application in large-scale civil structures, because it is difficult to find an appropriate sensor installation position on which the sensors can aim at the measurement point. Instead, GPS-RTK is widely applied to large civil engineering structures, but GPS-RTK has low sampling rate, accuracy and precision, and is frequently affected by environmental conditions such as bad weather and surrounded obstacles. In the study, a new structural response measurement system has been developed for the online measurement of displacement, velocity and acceleration of large-scale civil structures. The proposed system estimates the structural responses with high sampling rate, accuracy and precision by fusing measurements obtained from GPS-RTK and accelerometer based on two-stage Kalman filter. The proposed system has the following advantages over GPS-RTK sensors: (1) 6 DOF displacement, velocity and acceleration can be measured simultaneously with a single sensor system, (2) The accuracy (around 2 mm) and sampling rate (up to 100 Hz) of the proposed system are better than those of GPS-RTK, and (3) The performance of the system is less affected by environmental conditions. To verify the performance of the proposed system, a field experiments at Yeongjong grand bridge was performed.
The Influence of Periodic Arrangement of Structural Geometry to Ultrasonic Waves
In the current practice, the structural design and the damage detection methods are studied separately. However, the design can be integrated with the damage detection philosophy such that we can predict the damage presence through damage-induced imperfection in the design variables. New design approach is based on the placement of periodic subsystems, which have repetitive behavior from one subsystem (i.e. unit cell) to another. As each unit cell has the same geometric and materials properties, their frequency responses have similar characteristics until damage starts to form in a unit cell where the periodic behavior is modified. In this study, the periodic arrangements of different truss subsystems (used in bridges or tower designs) are studied using repetitive finite element approach. Their frequency responses are obtained for pristine condition and damage induced into different unit cells. The changes in the ultrasonic properties of each unit cell as functions of damage state are numerically and experimentally studied. The influences of non-periodic and period arrangements of truss systems to the ultrasonic wave are presented. It is shown that the periodic arrangement reduces the influence of boundary conditions to the frequency response of each subsystem, and the comparative approach of each unit cell behavior enables baseline-free damage detection approach.
Two-year Acoustic Emission Monitoring of Natural Corrosion in Prestressed Concrete Exposed to Saltwater
This paper reports on a project aimed at understanding whether acoustic emission (AE) data can be used to detect and classify corrosion in PC piles exposed to saltwater, ranging from steel depassivation to concrete cracking caused by the formation of expansive surface oxides. Experimental results are presented from three specimens representing portions of PC piles, which were directly exposed to saltwater through wet/dry cycles mimicking tidal action for up to 790 days. Corrosion activity was monitored continuously using AE sensors. Half-cell potential and polarization resistance were measured periodically to serve as benchmarks to detect steel depassivation and corrosion. Visual evidence of naturally-occurring corrosion at different stages was collected from strands that were extracted from decommissioned specimens.
Ultrasonic Guided Wave Inspection and Characterization of Integrated Circuit Packages with Delamination
Adhesive failure in an integrated circuit (IC) package occurs due to certain loading conditions or manufacturing induced defects such as the presence of residual moisture, foreign material or insufficient copper roughening, causing delamination between the layers and subsequent failure of the IC package. In this paper, the use of ultrasonic guided wave based nondestructive evaluation (NDE) technique is investigated to detect delamination in a large form factor IC package. Piezoelectric transducers are used as actuators and sensors and the signals obtained from controlled damaged specimens is analyzed to understand the effect of delamination on the guided wave propagation behavior. Matching pursuit decomposition (MPD), an advanced time-frequency based signal processing algorithm, is used to extract the associated guided wave features such as mode conversions due to delamination. The information is used to characterize features such as relative amplitude of the modes and time of flight. These results will be used to develop a robust damage detection framework for the rapid inspection and quality assurance of IC packages using a non-invasive NDE procedure
Wavelet-Based Wavenumber Filtering for Damage Detection and Thickness Estimation with Laser-Scanning
This paper presents the use of wavelet-based wavenumber filtering for damage detection and thickness estimation with Laser-scanning sensing. A piezoelectric transducer provides an excitation at a fixed, single frequency to a structure. Laser Doppler Vibrometer (LDV) with a mirror-tilting device measures subsequent steadystate structural responses, resulting in the full standing wavefield of the structure at the excitation frequency. After then, wavelet-based wavenumber filtering is performed to obtain dominant wavenumber components, which could be used for indicative of structural damage. Mapping processes based on the dominant wavelet-based wavenumber is carried out for damaged area visualization. To estimate the depth of damages, Lamb wave equation is transformed into the wavenumber-thickness equation with the known structural parameters. With this process, the thickness and the thickness reduction by corrosion was accurately identified. Several experiments are performed on thin walled structures, including pipes and plates, with different types of damage, including corrosion and cracks. The experimental investigation into quantitative assessment of welded joints are also performed. The results show that the techniques are very effective in damage size and depth estimation with improved speed.
Acceleration Response Data Processing for Structural Health Monitoring of a High-Rise Building
A 38-story steel moment-resisting frame building located in the city of San Francisco has been instrumented with 29 accelerometers for structural health monitoring. A data-recorder attached to the building basement is used to continuously store structural acceleration response data. Available recordings include both, ambient and earthquake acceleration response. In this study, three data processing techniques were applied to the collected data to define the baseline building vibration periods for monitoring of its structural condition. The power spectral density and the frequency domain decomposition techniques were applied to ambient vibration data whereas the eigensystem realization algorithm was applied to the earthquake data. In the application of these algorithms, input parameters including number of accelerometers, duration of recording, frequency resolution, and filtering of data were varied to study the variability of the results. This parametric analysis was applied to the estimated building periods to determine the level of confidence of the results. It was observed the estimated building periods are sensitive, especially near long period values, to input filtering and frequency resolution parameters. A high-pass filter was applied in the data processing to eliminate spurious period identification results, the cut-off frequency being defined based on stability of the filter algorithm. In regards of the frequency resolution, it was observed the estimated fundamental period results were scattered within a wide interval deviated from the mean when different resolutions were used. While a coarse resolution increases the precision or repeatability of the results among different datasets, a fine resolution is preferred as it allows for higher accuracy in the estimation of the structural periods. Based on the results, suggested parameters for building period estimation are listed and baseline periods are defined. The baseline periods serve as the basis to calibrate an FE model used for further studies.
Damage Assessment on Framed Structures Through Regression Models Retrieved by Nonlinear Numerical Analyses
Nowadays, Structural Health Monitoring is certainly a topic of great interest both in the research and in the professional field as evidenced by the large number of applications and systems installed all over the world. A permanent monitoring system could be installed in order to identify a possible damage occurred on framed structures after moderate/destructive earthquake. Furthermore, it could be an useful tool for evaluating the service condition and the remaining service life of a structure. Most of simplified methods for structural damage detection are based on the evaluation of the dynamic characteristics evolution associated to the fundamental mode of vibration of a monitored structure. Particularly, the variation of parameters such as eigenfrequencies, equivalent viscous damping factors and modal and/or operational mode shapes has been evaluated and analyzed in order to identify structural damage. Methods based on frequency variation can be applied to detect damage, but they are not able to localize structural damage. Instead, to this aims, methods based on the evolution of modal shapes and/or of their derivatives such as mode curvatures could be more effective. Aim of this work is the improvement of an existing method for damage localization on framed structures based on the evaluation of the mode curvature change associated to the fundamental mode of vibration during an earthquake. The approach is based on the use of a nonlinear filter, the band-variable filter, able to extract the nonlinear response of each mode of vibration. The paper focuses also on the possibility to quantify the damage occurred on the monitored structure by considering the correlation among maximum value of mode curvature variation and the maximum inter-story drift. The regression model between these two parameters has been defined through nonlinear numerical models for different reinforced concrete framed structures designed for gravity loads only, with and without the presence of infill panels.
Characterizing Dynamics of Pile-Supported Structures using System and Subsystem Identification Methods
Using a small number of modal parameters, we effectively describe the global dynamics of a centrifuge model comprising a pile foundation in improved soft ground. We applied subspace state space system identification (4SID) and transfer function methods to estimate the modal parameters from the pile bending moment data and accelerometer measurements of the soil and superstructure. Monitoring of the variations in modal parameters has been widely used to assess structural integrity and quantify damage. Our results show how the modal signatures of complex soil-pile-superstructure systems are governed by the modal properties of each subsystem and demonstrate that vibration-based methods can deliver practical SHM solutions for complex systems of this type
Dynamic Inference and Assessment of Structural Damage States Based on Naïve Bayesian Classifier
The monitoring, inference and assessment of structural damage states have become a significant problem to be studied. In this paper, a framework based on Naï Bayesian Classifier (NBC) is proposed to infer and assess structural damage states. The framework includes data collection, feature extraction and damage states evaluation. NBC is employed to find and create the correlation between Damage Sensitive Feature (DSF) and Structural Damage States Transition (SDST). The correlation helps to shorten and fasten the mapping procedure from DSF to damage states evaluation. A numerical simulation experiment of a two-story frame structure based on seismic wave input is presented. Firstly, the framework collects data obtained from accelerometer sensors and extracts maximum structural response from it as DSF. Subsequently, Story Drift Ratio (SDR) can be calculated as damage indicator to determine the damage state level of this two-story frame structure. A NBC model is created to simplify the mapping procedure and improves the accuracy. Finally, the experiment results prove the feasibility and efficiency of this framework. Meanwhile, NBC model is proved to be useful and its accuracy meets the requirements of online real-time dynamic structure damage states evaluation
Identification of Energy Loss Due to Soilstructure Interaction from the Recorded Vibrations of Buildings
The vibrations of a building are due to the waves that travel up and down the building. When the ground below the foundation is not rigid (e.g., hard rock), a portion of the downgoing waves from the lowest story is transmitted back into the ground. This transmitted portion corresponds to the energy loss due to soil-structure interaction. This paper presents an approach to identify such energy losses from the recorded vibrations of buildings. The approach is based on the discrete-time formulation of wave propagation in multi-story buildings subjected to vertically propagating shear waves. The wave propagation in each story is characterized by the upgoing and downgoing waves at the top of the story. We derive equations for the 2x2 transfer matrices that relate the upgoing and downgoing waves in one story to those in the adjacent stories. The downgoing waves at the soil-foundation interface gives a measure of the energy loss due to soil-structure interaction. Starting from the top story, and by consecutive use of story transfer matrices, we can relate the upgoing and downgoing energies at the soil foundation interface to those at the top story. This relationship is also represented by a 2x2 matrix. The matrix is basically the product of 2x2 story transfer matrices from the foundation to the top story. The inspection of the elements of the matrix indicates that this relationship is equivalent to an IIR (Infinite Impulse Response) filter, which can be represented by an AR (Auto-Regressive) model between the foundation response and the top story response
Monitoring Hand-Washing Practices using Structural Vibrations
In this paper, we present a novel system for monitoring hand-washing activities using vibration sensing of the sink structure. In the United States alone, more than 1.7 million patients each year are subjected to preventable infections in the hospital. Proper handwashing practices are essential for reducing these hospital-acquired infections. Existing approaches for monitoring hand-washing practices include visual observation, wearable sensing, and cameras. These approaches have been limited due to logistical challenges, deployment cost, and intrusiveness. Our system detects and monitors hand-washing activities by measuring the vibration response of the sink structure due to the hand-washing activities. We utilize the key insight that each activity associated with hand-washing (e.g. water running, soap dispenser actuating, hands rinsing with water, etc.) generates unique structural vibration responses in the sink structure. Our approach has the advantage of passively monitoring hand-washing activities through minimal cost-effective sensors with no requirements for additional staff or intrusive wearable sensors. The main research question is how to identify governing features that can distinguish the structural vibration responses due to each hand-washing activity. Our approach investigates the energy distribution of the vibration signal response from each hand-washing activity in the frequency domain. This approach uses the insight that each hand-washing activity generates different responses in the natural frequencies of the sink structure. Our method achieves an average classification accuracy of 95.4% with real-world experiments.
Performance of a Passively-controlled Steel Building Before and After the 2011 Great East Japan Earthquake
An eight-story Administration Building in Sendai City, Japan with oil dampers survived the 2011 Great East Japan Earthquake, but leaving the oil dampers on the first floor completely damaged. A novel retrofit plan using rubber bearings instead of oil dampers is proposed and tested, which are able to accommodate larger inter-story drifts. This building has experienced damage, rehabilitation and verification periods with different damping scenarios. For each scenario, the modal analysis is conducted to identify the dynamic characteristics and evaluate the performance of the structural frame and dampers. To identify the dynamic properties of the building, two kinds of modal identification techniques are implemented: Peak-Picking method (PP) combined with the half-power bandwidth method, the autoregressive with exogenous excitation (ARX) models method. The fundamental frequencies and damping ratios in two translational directions are extracted. Based on the identified results and long-term observation of seismic responses during various levels of earthquakes between 2003 and 2013
Seismic Induced Damage Detection through Parallel Estimation of Force and Parameter using Improved Interacting Particle-Kalman Filter
Standard filtering techniques for structural parameter estimation assume that the input force either is known exactly or can be replicated using a known white Gaussian model. Unfortunately for structures subjected to seismic excitation, the input time history is unknown and also no previously known representative model is available. This invalidates the aforementioned idealization. To identify seismic induced damage in such structures using filtering techniques, a novel algorithm is proposed to estimate the force as additional state in parallel to the system parameters. Two concurrent filters are employed for parameters and force respectively. For the parameters, interacting Particle- Kalman filter [1] is employed targeting systems with correlated noise. Alongside a second filter is employed to estimate the seismic force acting on the structure. The proposal is numerically validated on a sixteen degrees-of-freedom mass-spring-damper system. The estimation results confirm the applicability of the proposed algorithm
Damage Localization of a Real Structure using the Statistical Subspace Damage Localization Method
In this paper the statistical subspace damage localization (SSDL) method is employed in localizing the damage in a real structure, namely the Yellow frame. The SSDL method is developed for real testing conditions and tested in two damage configurations. It was demonstrated that the SSDL method can localize the damage robustly in the Yellow frame for simple and multiple distinct damage scenarios using the analytical modal parameters. The method is described and its effectiveness is demonstrated.
Structural Damage Assessment Through Both Centralized and Sensor-level Vibration-based Data Analysis
The concept of vibration-based damage detection (VBDD) provided a platform for structural system identification, damage detection and localization, and quantification. In this study the VBDD approach, through both centralized and sensorlevel data analyses, will be discussed for structural damage assessment. Damage assessment is focus on damage detection and localization using both ambient and the earthquake response measurements. Finally, for damage quantification a model updating technique called efficient model correction method (EMCM) is applied in this study to correct/update the analytical mass and stiffness matrix respectively using the identified frequencies and mode shapes from recursive subspace identification (RSI) algorithm. Combined the result from RSI and damage detection algorithms, the percentage of stiffness reduction and the time of damage occurrence can be identified. To demonstrate the capability of the proposed recursive subspace identification algorithm and the damage assessment technique, dynamic response data generated from the shaking table test of a twin tower steel structure is used to verify the algorithms on damage detection and quantification.
Structural Health Monitoring of Existing Bridges in Earthquake Prone Areas: Laboratory Validation
Road infrastructures and particularly bridges can suffer structural damage due to earthquakes threatening the efficiency of the transportation network and the possibility to ensure prompt rescue operation. This particularly applies to existing bridges, most of which have been designed and built according to outdated codes. Structural Health Monitoring (SHM) systems can support the prompt assessment of bridges after seismic events. However, reliability of modal based damage detection currently depends on the accuracy of modal parameter estimates automatically obtained from the analysis of the operational response of the monitored structure, and on the capability of the measurement system to resolve low amplitude as well as strong motions, eventually associated to saturation of sensors. In the present paper, the performance of a modal-based SHM system for existing bridges in seismic areas is assessed by shaking table tests on a 1:3 scale single span bridge representative of existing highway bridges built in the 60s in Italy. Results show that hidden damage can be identified on a remote basis, thus demonstrating the interesting applicative perspectives of modal based SHM for fast assessment of existing bridges in the early earthquake aftershock. The resilience to earthquake shaking of the SHM system has been also assessed. Finally, specific data processing procedures for earthquake response data are tested and compared with the results of laboratory measurements.
Electromechanical Impedance Method to Assess the Stability of Dental Implants
In this paper we illustrate the application of the electromechanical impedance (EMI) technique, popular in structural health monitoring, to assess the stability of dental implants. The technique consists of bonding a piezoelectric transducer to the element to be monitored. When subjected to an electric field, the transducer induces low to high frequency structural excitations which, in turn, affect the transducer’s electrical admittance. As the structural vibrations depend on the mechanical impedance of the host structure (in this case the implant secured to the jaw), the measurement of the PZT’s admittance can infer the progress of the osseointegration process. In the study presented in this article we created a 3D finite element model to mimic a transducer bonded to the abutment of a dental implant placed in a host bone site. We simulated the healing that occurs after surgery by changing the Young’s modulus of the bone-implant interface. The results show that as the Young’s modulus of the interface increases, i.e. as the mechanical interlock of the implant within the bone is achieved, the electromechanical characteristic of the transducer changes. The model and the findings of this numerical study may be used in the future to predict and interpret experimental data, and to develop a robust and cost-effective method for the assessment of primary and secondary dental implant stability.
Guided Wave Analysis of Osseointegration at Bone-prosthesis Interfaces
Advances in medicine have led to the development of prostheses that can be integrated directly to the host bone of residual limbs. Osseointegration of the prosthesis comes with many benefits including its use in patients with short residual limbs. There are also a number of challenges associated with their use including high rates of tissue infection, bone fracture, and uncertainty in determining when the limb can be loaded after surgery. This paper explores a practical sensing approach to monitor the healing process of osseointegrated prostheses using instrumentation attached to the percutaneous tip of the prosthesis. Specifically, guided wave methods are used to characterize the degree of bone growth and bone integration at the prosthesis interface. Prosthetic models consisting of titanium femoral stems implanted in artificial synthetic bones are employed to simulate the bone-prosthesis interface. Piezoelectric transducers are mounted to the percutaneous end of the prosthesis to introduce controlled body waves. The prosthesis acts as a wave guide allowing the guided waves to interrogate the bone-prosthesis interface inside the limb. Due to the constraints imposed by the prosthesis boundary conditions, three guided wave types are introduced including longitudinal, torsional and flexural waves. Tone burst signals were applied to a piezoelectric lead zirconate titanate (PZT) actuator array mounted to the rod to generate longitudinal waves in the prosthesis. Waves reflected in the prosthesis are received by the PZT array and analyzed to correlate changes in wave properties with implantation depth. Both numerical simulation and experimental testing are conducted to validate the use of reflected wave energy as a wave feature sensitive to penetration depth.
Numerical Simulation of Vibrational Methods for the Healing Assessment of an Internally Fixated Femur
Current bone healing monitoring techniques (X-ray, CT, manual manipulation) are subjective and qualitative. A quantitative monitoring technique for an internally fixated femur based on vibrational techniques is investigated. An intramedullary (IM) nail fixated femur will be at the focus of the investigation. The femur and fixation were modelled and their structural dynamics were solved using finite element analysis. This allowed visualisation of various mode shapes and identification of those most sensitive to healing. It was found that torsional modes were most sensitive to the stiffness change associated with healing. This data was then used as a guide for the placement of simulated accelerometer sensors and the simulated excitation point to predict the cross spectrum data that would be obtained during an experiment. Upon analysis of this data the torsional mode suggests that a relationship can be drawn between the natural frequency of the torsion mode and the state of health
Noncontact and Noninvasive Strain Monitoring of Osseointegrated Prostheses
The objective of this study was to develop a noncontact and noninvasive technique for monitoring the strain-states of osseointegrated prostheses. Knowing the stress- or strain-state is important, especially for ensuring patients comply with rehabilitation exercises post-surgery or for detecting prosthesis failure. The proposed method utilizes electrical capacitance tomography (ECT) coupled with a strain-sensitive thin film deposited onto osseointegrated prostheses. Here, ECT employs a set of noncontact electrodes, which can be used to excite a sensing region using a time-varying electric field, while capacitance measurements are simultaneously obtained. An inverse tomography problem can be solved, using capacitance as inputs, to reconstruct the permittivity distribution of the interrogated region. Since the permittivity of metals alone (i.e., prosthesis) is insensitive to stress and strain, a carbon nanotube-based thin film, which can be deposited onto the prosthesis surface prior to implant, was designed such that its permittivity is sensitive to applied strains. In this work, the ECT algorithm was first implemented and validated. Second, the thin film was deposited onto prosthesis surrogates, and the film-coated prosthesis was subjected to uniaxial tensile tests. ECT was performed at different strain-states to map, without making contact, the change in permittivity of the film-coated prosthesis. The results showed that ECT could identify the rod and detect changes in permittivity in response to applied strains. In general, the proposed tomographic technique coupled with strain-sensitive thin films can serve as a new noninvasive imaging and noncontact strain sensing modality.
“Smartâ€ Applications for Monitoring Percutaneous, Osseointegrated Implants
Percutaneous, osseointegrated implants (POIs)) are becoming more widely used in surgical implant procedures due to their ability to be directly attached to the skeletal structure. As these implants make their way through early-stage patient trials, there is an opportunity to use ideas from the field of structural health monitoring (SHM). SHM data-to-decision workflows provide a technological basis of detecting, estimating, and reporting conditions that could assist physicians and patients in producing actionable information. This particular work presents a computational study on how optimallyembedded strain sensors may be applied in-situ to estimate implant loads, which has implications on verifying both patient rehabilitation compliance and state awareness in the implant connection. An objective function based on a surrogate for Bayes risk is developed to derive the optimal strain sensing strategy
Detecting Damage in Grouted-joints of Wind Turbine Support Structures —Application to a Large-scale Experiment
SHM is a growing field for new and ageing infrastructure around the globe. Not least, since revenues for industry, operators and insurances are becoming increasingly obvious. Offshore wind turbines (OWT) represent an ideal application case – Being expensive, critical infrastructure with a remote, sometimes inaccessible, location, SHM can help to increase availability and reduce costs for operation and maintenance. Furthermore, with a tall slender structure and a large head mass, WT have the tendency to an extensive dynamic behavior, facilitating vibration based SHM. In the field of SHM for OWT and WT, diverse research has been carried out and some industrial applications e.g. for ice detection on rotor blades exist. In the offshore wind industry, grouted connections between piles and support structure have been adapted from the oil and gas industry. Nevertheless, the dynamic loading differs significantly form the one at large offshore platforms in the oil and gas industry and a deep understanding for damage initiation, progress and consequences is not yet fully achieved and under investigation [1]. Nevertheless, most research deals with axial loading, typical for Jacket structures, and the favored solution for OTW being monopoles with a loading dominated by bending. The presented large scale tests in the test center for support structures in Hannover Germany focus on damage in such connections. After investigating the influence of scour, marine growth and loosened bolts in the preceding project UnderwaterINSPECT [2], the current project QS-M Grout focusses on damages at grouted connections. For this purpose, two grouted connections in transition pieces (TP) are tested and damaged in the current project. Both, in a sand pit with a pile as footing and in a span where damage relevant cyclic loads are applied to the TP. The paper focusses on two main points: One is the influence and decrease of the TP stiffness along with cyclic loading, the other is the damage detection through vibration based SHM procedures. In the second case different condition Parameters will be compared to each other to evaluate the SHM-performance. The two TP were initially placed into the sand pit, where a reference load was applied by an electrodynamic shaker, mounted on top of the structure.
Extraction of Environmental and Operational Conditions of Wind Turbines using Tower Response Data for Structural Health Monitoring
Proper normalization of structural response data that accounts for the environmental and operational conditions (EOCs) of a structure is a key step in structural health monitoring (SHM) analyses. Normalizing data based on EOCs enables a more effective comparison of damage sensitive features extracted from structural response data derived from a system operating under a wide variation in its operations. In this paper, structural response data from an operational wind turbine is used for both damage detection as well as for EOC-based data normalization in a damage detection framework. The structure under consideration is the tower of a 3-kW wind turbine located at Los Alamos National Lab. A wireless monitoring system was installed in the turbine tower to record the tower acceleration response when the tower was undamaged and intentionally damaged. Gaussian Process Regression is used to find tower response features that correlate with EOCs, specifically rotor angular velocity and nacelle yaw angle, yet are insensitive to structural damage. The features are then used in the damaged detection framework to enhance the performance of clustering algorithms used for EOC normalization. Damage-sensitive features are then used as condition parameters for damage detection. The efficiency of the proposed EOCs normalization process is evaluated by comparing the Receiver Operating Characteristic (ROC) curves of a threetier damage detection strategy previously proposed for wind turbine systems
Fatigue Usage Monitoring in Wind Turbines using Sparse Vibration Measurements
As the size of wind turbines continues to grow upwards of 10MW and the tendency to move offshore gains momentum, maintenance and repair operations become more difficult and costly. One consequence of the increased size and harsh operational environment is the increased potential for fatigue failure of structural components. Development of structural health monitoring systems capable of tracking fatigue damage is desirable. This type of system can be coupled with control mechanisms to simultaneously maximize generation and minimize operational loads which lead to fatigue damage. Various researchers have proposed the use of vibration measurements to monitor stresses and fatigue damage in the tower and blades of turbines. This paper proposes a finite element model-based state estimator that explicitly accounts for spatial correlation and the statistical properties of the excitation through knowledge of the underlying power spectral density of the wind loads. The proposed state estimator combines noise contaminated measurements of the structural response with high-fidelity finite element models to estimate the time history of stress and strain throughout the complete structure of the turbine. The approach is used to estimate loads and stresses (and their associated uncertainty) in the tower of the NREL 5MW reference turbine subjected to turbulent wind. In this paper, the method is implemented in a simulated environment where the system is a FAST model of the turbine and the estimator is formulated using a simplified finite element model of the tower. The results show that under these conditions the proposed model-based estimator provides satisfactory tracking capabilities without the need to explicitly measure or estimate wind loads.
Field Demonstration of Radar-based SHM of Wind Turbine Blades at a 2 MW Wind Turbine: Installation, Data Acquisition and Signal Analysis
This paper reports on a radar-based structural health monitoring (SHM) system for wind turbine blades. Initial results are presented from a field installation at a 2 MW wind energy plant (WEP) where two FMCW radar systems with high output power are placed at the tower of the WEP. The frequency bands are 24.05-24.25GHz and 33.4-36.0GHz, respectively. The remote sensing inspection takes place when the blades pass the sensor system during normal WEP operation. Here, we present the concept and realization of this innovative SHM system, the synchronous data processing chain as well as experimental data from both radar sensors
Fleet Monitoring and Site Specific Environmental and Operational Conditions in Wind Energy
In wind energy, the objective of fleet monitoring is to achieve the best possible energy yield and to early detect upcoming defects or not optimally adjusted turbines. If a vibration-based SHM system like SHM.Blade® is installed on all turbines of a wind park, an upgrade to fleet monitoring is possible. In the case of fleet monitoring, the vibration level of all turbines in a wind farm is related to their current yield. In general, the assumption is that all turbines in a park have more or less the same energy input and therefore can generate the same amount of electrical energy. Significant deviations from this, caused e.g. by false control, aerodynamically problems, mass imbalances, or damages, reduce the capacity of electricity production. A wind turbine is designed to convert the kinetic energy of wind into torque of the drive train. Therefore wind speed is the most decisive parameter for energy production of a turbine. The amount of energy that can be obtained from wind increases under ideal conditions with the third power of the wind speed. However the wind speed on site depends on different influences like e.g. turbulences in the lee of another turbine, wind park related difference in the height above sea level of individual turbine sites and the roughness of the ground. Therefore you cannot compare the energy yield of two turbines even if their locations are only some few 100 meters apart. The site specific difference in wind speed respectively the site specific efficiency is too big. Figure 3 shows the positions of three wind turbines in an existing wind park. The distance between the turbines is about 500 meters. However the efficiency factor related to the situation in the wind park differs considerably between 90,8% and 95,7% (see TABLE ). That means even very few meters difference with the height of the site and differences of roughness etc. can add together to an amount which is bigger than e.g. the influence of a misadjustment of the pitch angle. For that reason site specific Environmental and Operational Conditions (EOCs) have to be eliminated first before doing successful fleet monitoring. The paper will exemplify by means of real examples the importance of fleet monitoring and the necessity of EOC consideration within fleet monitoring of wind turbines
Improvement of the Damage Detection Performance of a SHM Framework by using AdaBoost: Validation on an Operating Wind Turbine
In SHM applications various damage-sensitive features can be used for making decisions regarding damage detection. In all cases, classifiers evaluate the results and make a final decision regarding the state of the structure. Often, there are discrepancies among the decisions of different classifiers, resulting in different detection performances for each damage feature. This is expected as different classifiers may be better suited for different data settings, even in data sets corresponding to the same system. Boosting algorithms combine multiple base classifiers to produce an ensemble, whose joint decision offers a better performance than any of the base classifiers. Adaptive Boosting (AdaBoost) is deployed in this paper to build a strong classifier based on the classifiers of a three-tier modular SHM framework for improving detection performance. The framework consists of three parts: application of machine learning clustering algorithms for data normalization, feature extraction and hypothesis testing (HT). Each connection of damage feature, also referred to as condition parameter (CP), and HT composes a classifier that can be used as a weak classifier in the boosting algorithm. Information from the SHM framework classifiers is used, in order to build a strong classifier that is able to classify the value of any CP and improve the detection performance. The integration of AdaBoost with the three-tier SHM framework is validated on an operating 3 kW wind turbine. The results are demonstrated in receiver operating characteristic (ROC) curves with AdaBoost increasing the performance of damage detection.
Monitoring of Wind Turbine Structures using Stationary Sensors and Short-term Optical Techniques
This research deals with monitoring of a large onshore wind turbine (3 MW, 142.5 m hub height) with a hybrid (steel/concrete) tower design. The structural dynamics of the tower are monitored by stationary sensors installed inside the tower and short-term optical techniques. The insights are used to calibrate a detailed finite element model of the tower. Combining measurement data and a finite element model, the overall objective of the project can be reached, which is to detect defects and fatigue and to estimate the remaining service-life of the wind turbine support structure. Modal parameters of the structure including uncertainties were obtained by operational modal analysis of 244 10-min. vibration monitoring datasets. The resulting first and second order bending eigenfrequencies (0.27 Hz and 1.17 Hz) were deployed for validating and improving a finite element model by adding initial displacement stiffness. Besides validation using modal parameters a comparison of the structural response from field measurements and FE model is scheduled. Therefore tower displacement is determined from acceleration and digital image correlation of video data. Test measurements at the full-size test wind turbine were carried out providing coinciding displacement values
On the Structural Health Monitoring of Operational Wind Turbine Blades
As the wind energy sector evolves, blade manufacturers have started to produce larger blades in an effort to maximise power production. As the blades are amongst the most important and most expensive constituents of the wind turbines, it is beneficial to monitor their structural integrity, which would aid in minimising downtime due to periodic maintenance, and/or damage. The work carried out in this paper is based on real data obtained from a Siemens Wind Power wind turbine using sensors on the blades. During normal operation, the blade sensors capture a range of frequency content aside from the blade frequency modes. These include the vibration contents of the turbine rotation speeds (which are the most dominant), the harmonics, and that of the tower. To add to the complexity, since wind speed fluctuations affect the rotor speed, wind turbine operations are nonstationary, making structural health monitoring (SHM) a difficult task. Therefore, this present work aims to develop an SHM technique, which utilises machine learning techniques, to reliably determine the structural integrity of the blades with respect to certain damage modes.
SHM of a Small Wind Turbine Unit by Assessment of Vibration Behavior
Small wind turbines are generally supplying green energy for individual consumers, in more or less isolated locations. This fact creates the need to combine simplicity and efficiency of design with reliable running and easy exploitation. The paper presents effective and affordable ways meant to avoid or detect dangerous vibrations of the quite complex structure of a wind turbine unit, incorporating the runner, the turning platform and the supporting tower, the last structural element also housing solar panels and water tanks.
The Mistralwind Project—Towards a Remaining Useful Lifetime Analysis and Holistic Asset Management Approach for More Sustainability of Wind Turbine Structures
In the near future more and more onshore wind turbines will reach the end of their designed service life of twenty years. From an economic and operational perspective, it can be profitable to prolong the operational activities beyond the original design life. In particular, turbines in the megawatt class bear technical and economical reserves, which should be yielded for more sustainability of wind turbine technology. Prospectively it might be possible to operate wind turbine structures longer than thirty years with a service life optimizing operation and maintenance strategy. The MISTRALWIND project (acronym for Monitoring and Inspection of Structures of Large Wind Turbines; partners: IABG Industrieanlagen-Betriebsgesellschaft mbH, Technical University Munich, Siemens AG) focusses its research activities on developing innovative and reliable methods and sensor systems for analyzing the remaining useful lifetime (RUL) capacities of a wind turbine structure. The paper is aimed at giving an overview of the first project results in the areas of FEM-modeling, wired and wire-less sensor technology, non-destructive testing techniques, fatigue mechanisms and integrating information from monitoring and inspection into the turbine’s controller, and probabilistic models for optimized operation and maintenance strategies for wind turbines. The methods developed here can be transferred to other structural systems – e.g. civil engineering structures or aerospace structures – to optimize their operation and maintenance strategy
Wind Turbine Blade Damage Detection Via 3-Dimensional Phase-Based Motion Estimation
As a typical non-contact sensing and monitoring capability, optical measurements have been gaining tremendous attention recently in structural dynamics identification and health monitoring. This non-contact methodology usually supplies full-field measurement, and is able to out-play traditional transducers such as accelerometers, laser vibrometers and strain gauges. As a new computer vision based approach, 2-D Phase-Based Motion Estimation (PME) is extended in this paper to 3-D applications for structural health monitoring (SHM) purposes. Compared to the 2-D PME, which only captures the in-plane motions on the selected field of view of the camera, 3-D PME removes these restrictions and estimates the out-of-plane motion partially if the required conditions are met for the proposed algorithm. The modal information such as natural frequencies of the test wind turbine blade are extracted and compared with the results obtained via 2-D PME, as well as other conventional measurement approaches. With all the modes decoupled on the testing turbine blade, a contrived damage is added to the structure, and the 3-D PME proposed in this paper successfully extracts the modal frequencies for both conditions, baseline and damaged, and therefore detects damages in a non-contact and full-scale fashion.
A Multifunctional Piezo and Temperature Sensor for Process and Structural Health Monitoring of CFRP Structures Made by Resin Transfer Molding
Within this paper, the authors present a novel multifunctional piezo – temperature sensor concept for flow front, cure and structural health monitoring of composite parts. The multifunctional sensor is made of piezo disc in series to a Pt100 temperature sensor used a shunt resistor. In order to measure the impedance of the Piezo a signal generator is connected to the ground electrode of the piezo and the PT100 temperature sensor. The current through the piezo is measured via the Pt100. For actuation, a frequency modulated chirp signal is used and the response voltage over the PT100 sensor is measured. Out of the FFT of the voltage response, the impedance of the piezo sensor can be determined. The developed sensor concept offers several advantages. Only one data acquisition unit for process and SHM is required. The time resolution of cure monitoring can be drastically reduced. The integration of a Pt100 as shunt resistor allows the measurement of the temperature during the manufacturing and usage of the part, where the knowledge of the temperature distribution can be used for effective temperature compensation techniques during SHM with guided waves.
A Steerable-Needle Inspired Mechanism for Inspecting Extremely Confined Spaces
We present a novel, compliant mechanism that provides the capability to navigate extremely confined spaces for the purpose of infrastructure inspection. Extremely confined spaces are commonly encountered during infrastructure inspection. These spaces can include pipes and ventilation ducts. Often these infrastructure features go uninspected simply because there is no viable way to access their interior. In addition, it is not uncommon for extremely confined spaces to be laid-out in a maze-like fashion that must be selectively navigated in order to execute an inspection. In this work this problem is addressed by taking inspiration from the field of steerable needles. Steerable needles are typically made from super-elastic nitinol. This material has the property that it can experience high strain without yielding. This property makes superelastic nitnol an attractive material for navigating a tube through a maze-like arrangement of extremely confined spaces
Challenges and Solutions in SHM for Nuclear Power Environments
As the nation’s nuclear fleet looks to life extension, advanced reactor (AdvRx) concepts are being considered as alternatives that may help meet anticipated energy needs. AdvRx concepts include small modular reactors (SMRs) where the electrical output of a module is generally less than 300 MWe, and Gen-IV reactors that utilize non-light water as coolants for heat removal from the reactor core. Several of these AdvRx concepts incorporate challenging environments for sensors, with core outlet temperatures in excess of 500°C, fast-spectrum neutrons that can accelerate damage of materials and sensors, and corrosive coolant environments. In nuclear power plants, information on component condition and the failure probability associated with degraded components is considered critical to maintaining adequate safety margins and avoiding unplanned shutdowns, both of which have regulatory and economic consequences. In AdvRx, the relatively smaller levels of operational experience (when compared with the existing fleet of light-water-cooled reactors) and limited knowledge of physics-of-failure mechanisms in AdvRx environments, when combined with the potential for increased degradation rates, point to the need for enhanced situational awareness with respect to critical systems and components. Key to enhanced situational awareness in these plants will be sensors that can survive the extreme environments (temperatures between 500°C and about 900°C, lifetime fast neutron fluences in excess of about 1020 neutrons/cm2), algorithms for diagnostics of system or component condition and prediction of remaining lifetimes, and techniques for informed decision making that enable optimal decisions on plant operations and maintenance actions. This paper describes recent and ongoing research that addresses these challenges. While the application context for these developments is in the nuclear power community, we believe that these developments can be applied to meet emergent needs in a number of different application spaces.
Design of a Structural Health Monitoring (SHM) System for the Third Bridge Over the Panama Canal at the Atlantic Side
A signature bridge is an important part of a nation’s infrastructure because of the function it serves, but also because its attests to a country’s economic strength and technological advancement. Panama is building a major cable-stayed bridge with a design life of in excess of one hundred years so it would be beneficial to monitor it so that any departure from assumptions made during design are detected early. During its service life, circumstances and conditions may change, resulting in different types and magnitudes of live load, material variations, natural disasters, and human factors. To ensure safety, functionality and durability of the bridge, it is important to have a comprehensive understanding of the reliability of its structural components. Therefore, a Structural Health Monitoring (SHM) system is needed. This report outlines a proposed SHM plan for what will be the world’s largest cable-stayed bridge with a concrete superstructure – The Third Bridge over the Panama Canal at the Atlantic Side. The proposed bridge, which has a 530 m main span, is located in Colon, Panama, one of the most corrosive environments in the world. The report outlines the overall objectives of a SHM plan, the design principles, and recommended types and locations of sensors. The proposed SHM system includes the following: sensor arrays, a data acquisition and transmission system, data processing and control, a health diagnosis methodology, early warning alarms, and a security assessment process. An important focus of the system will relate specifically to durability and the monitoring of corrosion in reinforced concrete. Data collected will support and help optimize decision-making on future maintenance and repair. Tension force monitoring in cable-anchorage system will be proposed as well, to detect changes that might occur due to corrosion or fatigue.
Experimental and Simulation Studies of Underground Wireless Sensor Networks
This paper summarizes the experimental and simulation results in our studies of buried wireless sensor networks (B-WSN). The goal is to choose the suitable Industrial Scientific Medical Band (ISM) frequency which can be applied in the underground environment and then to investigate the underground communication ability of wireless modules based on the chosen frequency. As a key factor in point to point radio communication in WSN, the RF path loss in soil was evaluated and the results show that the attenuation significantly increases with high operating frequency. The penetration depth versus dielectric constant with various operation frequencies was also studied in this research. The simulation result shows that the penetration depth decreases with high moisture content and high operation frequency. Simulations indicate that the operation frequency in Sub-GHz range can acquire higher penetration depth and lower path loss compared to other high operation frequencies such as 2.4 GHz. The experimental results indicate that RSSI is significantly negatively related to the depth of filled soil. The results indicate that the maximal above ground horizontal transmission distance is 1.5m when the node was buried 1.3m underground
Gasifier Health Monitoring using Smart Refractory Bricks
The short life of the refractory lining in the slagging gasifier is a major concern leading to high operating cost and low availability. For prolonging the life of the refractory lining, it is important to monitor its health. Two key variables for monitoring the health of the gasifier refractory are the temperature profile both in the axial and radial directions along the wall and the extent of slag penetration in the wall. However, stateof- the-art measurement techniques are inadequate for the harsh gasifier environment. In this paper, a novel approach is considered where sensors embedded in a brick, denoted as the smart refractory brick, is used to estimate these two variables. Typical approaches, where correlations between the raw measurements (such as the voltage, current, etc.) and the variables of interest (such as the temperature) are used, are inadequate due to the high spatial temperature gradient along the wall and time-varying material properties. In this paper, a model-based estimation approach is developed to address this issue. Issues with the out-of-sequence measurements in the estimation framework are addressed.
Influence of Gamma Radiation on Piezoelectric Active Elements of Space Systems
As more structures enter orbit for long-term use or for short-term missions, safety of space vehicles becomes increasingly important. Structural health monitoring (SHM) can play a significant role in achieving the goal of safe space flight as well as reducing orbital debris. Before SHM is implemented in space structures on orbit, it is imperative to understand the effects of the extreme space environment on elements of the SHM system. Elastic wave propagation and electro-mechanical impedance spectroscopy are among the most promising SHM methods and could be implemented for on-orbit applications, provided effects of space environment are compensated. Piezoelectric sensors are susceptible to elements of the extreme space environment, such as temperature changes, vacuum, and radiation. Effects of temperature, vacuum, and radiation on piezoelectric sensors were experimentally investigated during electro-mechanical impedance testing in simulated space environment. In this paper, the authors present data from gamma radiation experiments and discuss the possible mechanisms at work in the changes in impedance signatures of the sensors. A theoretical model was used to identify the parameters of the piezoelectric constitutive equations that are affected by gamma radiation exposure. The proposed approach allows for compensating effects associated with space environment and may find utility in design and development of SHM systems for space applications.
Robust Variable Input Observer for Structural Health Monitoring of Systems Experiencing Harsh Extreme Environments
Systems experiencing events in the order of 10μs-10ms timescales, for instance highrate dynamics or harsh extreme environments, may encounter rapid damaging effects. If the structural health of such systems could be accurately estimated in a timely manner, preventative measures could be employed to minimize adverse effects. Previously, a Variable Input Observer (VIO) coupled with a neuro-observer was proposed by the authors as a potential solution in monitoring their structural health. The objective of the VIO is to provide state estimation based on an optimal input space allowed to vary as a function of time. The VIO incorporates the use of mutual information and false nearest neighbors techniques to automatically compute the time delay and embedding dimension at set time intervals. The time delay and embedding dimensions are then used to vary the input space to achieve optimal performance for the estimator based on the observed measurements from sensors. Here, we augment the VIO with a smooth transitioning technique to provide enhanced robustness. The performance of the algorithm is investigated using experimental data obtained from a complex engineering system experiencing a harsh extreme environment. Results show that the enhanced VIO incorporating a smooth transitioning input space outperforms the previous VIO strategies which allowed rapid input space adaptation
SHM in CMS Underground Detector at CERN using FBG Sensors
The results of structural and thermal monitoring of the Compact Muon Solenoid (CMS) experiment in the underground site at CERN will be reported. The measurements are carried out by means of Fiber Bragg Grating sensor arrays installed on the CMS detector, from the inner to the outer regions. This fiber optic monitoring system represents the ideal solution to secure a reliable and accurate sensing system to be used 24/7 in the harsh environment at CERN
Simultaneous Strain and Temperature Sensing using a Microstrip Patch Antenna
In this paper, we present simultaneous strain and temperature sensing using a single microstrip patch antenna fabricated on commercial printed circuit board (PCB). Temperature and strain are extracted from the resonant frequencies of the patch antenna as these frequencies are dependent on the dielectric constant of the PCB and the patch dimensions, which are subject to change with applied strain and temperature variations. The principle of operation of the patch antenna is first discussed, followed by the design and fabrication of the instrumented test specimen. Finally, thermal and mechanical tests were conducted to validate the strain and temperature sensing performance of the antenna sensor
Temperature Resistant Fiber Bragg Gratings for Online Monitoring of Future Sodiumcooled Fast Reactors: Paving the Way to SHM Implantation into the Nuclear Industry
Advanced manufacturing techniques using thermal engineering (regeneration process) and ultrafast laser micromachining of silica optical fibers have tackled the challenges of making high temperature resistant Fiber Bragg Gratings (FBGs) sensors. Arrays of temperature and strain wavelength-multiplexed FBGs sensors for the online monitoring of future Nuclear Reactors (Generation IV) are becoming a reality. Both regenerated and femtosecond FBGs have been intensively tested up to 950 °C for temperature mapping and recently tested also up to 400 °C for strain measurements on metallic devices. These sensors are expected to perform continuous monitoring of sodium-cooled nuclear reactors and to improve their safety and pave the way to Structural Health Monitoring (SHM) implantation into the nuclear industry. We present the manufacturing of i) regenerated FBGs using an all-optical process and of ii) femtosecond laser-written point-by-point femtosecond (fs PbP) FBGs directly into free-standing singlemode optical fibers. The presented techniques may not only speed up the manufacturing process but also improve the mechanical reliability of the FBGs. The temperature stability of both kinds of FBGs is assessed through long term ageing up to 950 °C highlighting their temperature resistance. Both are appropriate candidates for online thermometry even in severe environments such as those encountered in future Sodium-cooled Fast Reactor. Strain sensing is under development using fs PbP FBGs to operate beyond 400 °C using a traction machine and standard metallic specimens.
Anomaly Imaging using Decomposed Lamb Wave Modes
This paper presents a tomography-based method for structural health monitoring (SHM) using guided Lamb waves. The method assumes an active SHM system in which the structure is excited sequentially using a set of piezoelectric transducers attached to it. The resulting waveforms are collected at the remaining sensors on the structure for further analysis. The algorithm compares waveforms collected at inspection time to baseline signals corresponding to the pristine state of the structure and defines an appropriate anomaly index. The anomaly index used in this work is based on the attenuation of the first arriving mode component. The damage indices associated with all actuatorsensor pairs form the input to an algebraic reconstruction tomography algorithm that reconstructs an anomaly map for the structure. The reconstruction is performed using a sparse recovery method known as basis pursuit denoising assuming, that the anomalies are relatively small compared to the size of the structure. The reconstructed map is further processed to estimate the boundaries of the anomaly on the structure. Experimental results for estimating the outline of aluminum masses attached to a unidirectional composite plate are presented in this paper. These results indicate that our approach works better than traditional approaches in accurately estimating the extend of anomalies outline in composite structures.
Automated Acoustic Scanning of Concrete Bridge Decks
Chain drag testing is commonly used in current practice for bridge deck evaluation due to its low cost and ease of use. However, this method is subjective, and highly depends on the experience of the operators. An automated acoustic scanning system is developed to detect delaminations in concrete bridge decks. The system consists of an array of chains or impactors, a noncontact MEMS microphone sensor array, multichannel data acquisition device, RTK GPS positioning system, and signal processing schemes. Acoustic signal collected by the microphone is processed by short time Fourier transform (STFT). STFT amplitudes in the frequency range from 0.5 kHz to 5 kHz is summed up to form one dimensional data set vs. scanning time for each channel, and then data sets for each channel are stacked together to generate a two-dimensional map. A delamination identification algorithm is then developed to identify the center positons, dimensions of delaminations and recognize false positive delaminations. This system is validated on a 300 feet long bridge near Lincoln NE.
Characterizing the Effect of Applied Stress in Concrete by Magnitude-Squared Coherence of Ultrasonic Full-Waveforms
Ultrasonic testing is widely used to evaluate concrete structures. Namely, the ultrasonic pulse velocity method, which is based on the time-of-flight between transducers, is used in practice to determine the uniformity of concrete in structural members. While this approach is capable of detecting relatively significant deterioration, it is insensitive to small internal changes, such as a geometric change due to an applied stress. The objective of this paper was to study the effect of applied stress in concrete on ultrasound using digital signal processing on the recorded fullwaveforms. For this purpose, the magnitude-squared coherence (MSC) was used to determine the similarity between two recorded waveforms: a reference recorded waveform under no stress and one taken at a certain level of applied stress. The experimental work for this study consisted in applying a monotonically increasing compression load on four Ø 152 x 305 mm concrete cylinder specimens up to failure. A pitch-catch setup was used during the loading process consisting of two identical ultrasonic normal-wave transducers to continuously transmit an ultrasonic pulse and receive its response. The results show that looking at the recorded data in a traditional fashion does not show any differences before an ultimate load of approximately 80% is reached. By using MSC to compare entire recorded waveforms, however, a linear relationship to approximately 70% of ultimate stress can be observed immediately, starting at very low levels of applied stress.
Clutter Effect on the Noncontact Seismocardiogram Signals Measured using Microwave Radars
Seismocardiogram (SCG) signals, the measurements of local chest wall vibrations in response to the heartbeat, have been used to evaluate the mechanical aspects of cardiovascular health. A noncontact sensor based on microwave Doppler radars provides a potential approach to monitor such chest wall vibrations without attaching a sensor to the body. During detection, the chest wall displacement induced by the heartbeat will exhibit in the phase variation of the microwave signal, from which SCG signals can potentially be extracted and the cardiovascular characteristics can be analyzed. However, the microwave Doppler radar approach suffers from stationary clutter, which is the reflection of the background microwave signals. Radar clutter can decrease the ratio of effective signals in the receiver, weaken the effectiveness of the signal, and deteriorate the effective signal to noise ratio. An experiment is set up to evaluate the clutter effect on the SCG signals measured by the noncontact microwave radar sensor and variation of systolic time intervals (STIs) extracted from the noncontact SCG signals under physiological perturbations. The experimental results show that, with a high clutter level, the correlation and similarity in waveform shapes of the SCG signals measured by the noncontact sensor and the contact sensor are reduced, compared with the results when the clutter level is low. In addition, the changing direction of STIs may be influenced by the high level clutter. Therefore, it is important to design a clutter removal strategy to increase the measurement accuracy of the noncontact SCG waveforms so that the cardiovascular behavior can be effectively captured.
Data Fusion Application in Predicting Human Comfort
This paper studies the use of wearable device data along with other parameters such ambient temperature to model human comfort. Several machine-learning methods were used to build thermal comfort models from five individual’s wearable biometric data and their surrounding ambient conditions. The effects of the machine learning and input feature type and the output class size on the model accuracy were investigated. It is the goal to determine exactly what combinations of these factors will be able to accurately predict human thermal comfort. The accuracy of these models was determined by comparing their prediction to the individual’s actual thermal comfort found using voting input
Experimental Validation of an Improved Dynamic Displacement Estimation Method using Accelerometer and Gyroscope
Dynamic displacement of a structure is an important indicator of structural health condition after an earthquake. The displacement sensors, such as the linear variable differential transducers, cannot be used with an earthquake because the displacement of the instrument is hard to be separated from those of the structure. Recently, many new low-cost microelectromechanical sensors have both 3D accelerometers and 3D gyroscopes on board. These sensors provide simultaneous and high accurate measurements. In this paper, we will utilize these sensors to estimate dynamic displacement. Specifically, we firstly propose to use a complementary filter to combine the acceleration and gyroscope signals. Then, we apply digital signal processing techniques to improve the estimation accuracy further. The proposed algorithm shows highly accurate results on a data set obtained from an indoor shake table experiment
Intelligent Aperture Identification Combining Compressed Data Acquisition with Sparse Filtering-based Deep Learning Towards Natural Gas Pipeline Leak
Aiming at the problems of natural gas pipeline leak monitoring, it proposed an intelligent pipeline leak aperture identification method combining compressed sensing (CS) and deep learning theory, which can achieved compressed sampling, adaptive feature extraction and recognition. The random Gaussian matrix was applied to realize the compressed data acquisition, and the sparse filtering based on deep learning was applied to achieve the automatic selection of the features. Finally, the high precision recognition of apertures was implemented by softmax regression. Experimental results showed that this method achieved the compression of the monitoring data, and the identification performance for data of compressed sensing domain was better than traditional methods.
Interference and Removal of Respiration Harmonics on Noncontact Seismo-cardiogram Signals
The microwave radar technique is a promising noncontact method for detecting seismocardiogram (SCG) signals, which can provide valuable information regarding the mechanical aspects of cardiovascular health. The noncontact method takes the measurement without directly attaching sensors to the skin, therefore providing a less obtrusive and more convenient solution for the patient. Our preliminary investigation has demonstrated the ability of this noncontact approach to capture the timing features of the SCG waveform. However, since the chest wall motion measured by the noncontact microwave radar sensor contains both respiration and heartbeat patterns of a human subject, higher order harmonics of the respiration signal might severely contaminate the noncontact SCG signals. Since the respiration harmonics are discretely spread in the full frequency band of heartbeat signals, comb filters are adopted to remove the respiration harmonic interference. To achieve the goal of reducing interference of respiration harmonics and at the same time preserving as much cardiac information as possible, the comb filters are designed to have a steep slope and a narrow bandwidth. The position of the respiration and its harmonics are obtained with a complex demodulation method and are used as the input parameter of the comb filter. A comb filter with a shelving filter order of 10 and a bandwidth of 0.024 Hz, which can filter out the respiration harmonics up to the 10th order, is demonstrated to be effective in reducing the interference of respiration harmonics and in improving the capability of extracting the change of systolic time intervals from the noncontact SCG signals in the presence of physiological perturbations.
Investigation on Mapping Relationship Between Deflection and Cable Tension by Artificial Neural Networks
Cables usually serve as critical load-bearing structural components in cable-stayed bridges. Vertical deflection is an important overall indicator that directly reflects the health status of bridge. The relationship between cable tension and vertical deflection also reflects the underlying mechanism of structures. In this context, a machine learning-based approach is proposed. Cable tension and vertical deflection are caused by prestress, temperature and vehicle loads. In this study, we focus on the cable force and vertical deflection caused by vehicle loads. Artificial neural network (ANN) is employed as the supervised learning method to learn the relationship between cable tension and vertical deflection. Field monitored data of Jintang Bridge are used to test the validity of the proposed method. The results show that ANN is capable of modelling vertical deflection from cable tension data in the case of arbitrary vehicle distribution. The proposed method which integrates the local and global variables by using two different kinds of monitoring data, avoids the complicated mechanical derivations, and is proved to be environment-insensitive.
Magnetic Flux Leakage Detection Defect of Oil Storage Tank Applying Variational Mode Decomposition
Corrosion, stress and mechanical damage of oil storage tank can lead to disastrous consequences. Therefore, it is very important to study a reasonable and effective method for detecting tank defect. Signal processing and defect recognition technique is one of the most important techniques in oil tank inspection magnetic flux leakage system, the paper discussed its signal processing procedure. First the noise robustness of the variational modal decomposition (VMD) is analyzed, and compared with wavelet decomposition and empirical mode decomposition (EMD) respectively to verify the superiority of VMD in signal-to-noise ratio. Especially in the low-band signal decomposition processing VMD has obvious advantages. Detection is then discussed the application of the magnetic flux leakage method in the tank defects, and verified the feasibility by the magnetic flux leakage detection in a laboratory platform. Finally, the VMD algorithm is used to decompose and reconstruct the magnetic flux leakage signal of the oil storage tank wall, and the leakage magnetic signal is effectively decomposed to achieve the de-noising effect and the modal function of the required frequency band is extracted to achieve the purpose of denoising. The validity of the proposed method is proved by means of the magnetic flux leakage detection technique based on VMD .
Passive Defect Detection and Imaging in Structures by Cross-correlations of Infrared Thermography Measurements
Infrared Thermography (IT) is one of the most common techniques employed for Non-Destructive Evaluation (NDE) of structures, particularly due to its wide area coverage and imaging potentials. Its drawbacks, as the need of a powerful heat source and set-up, could be overcome if applying the technique in a passive mode. In this work, the feasibility for passive Green’s function retrieval from diffuse thermal field is investigated. Relying on some wave propagation aspects, it is proven that a thermal Green’s function can be reconstructed using long-time cross-correlations of temperature time histories, recorded from an IR camera. The random thermal fluctuations of the structure would still contain information related to the heat propagation path and hence could be exploited to detect anomalies. A successful application of this concept would allow defect detection and imaging, avoiding external sources. This technique has been already applied in many other environments, as seismology, oceanography and structural health monitoring, relying on acoustic wave fields. In the thermal case, the exploitation of the already available 2D pixel grid resolution as a large temperature array would also allow beamforming, due to its large aperture. This paper will discuss the feasibility of passive thermography and present ongoing studies in aluminum and composite plates with engineered defects
Recursive Multi-step Prediction of Bioimplants Thermal Effect
As the implantable devices, like neural prostheses, become more powerful, the temperature increase caused by their operation is becoming a growing concern. For example, it is reported that a temperature increase greater than 1 C could damage the surrounding brain tissue for neural implants. Thus, it is important to model the thermal effect of implantable devices. Previous literatures model the heat transfer from the bioimplant to its surrounding tissue using the famous Pennes bioheat equation, which are then solved by methods like the Finite Difference Time Domain (FTDT). However, these methods have significant computational cost, which makes them not suitable for applications where online thermal effect prediction is required. Moreover, due to the time varying nature of the human body and the complicated relationship between the power dissipation of bioimplant and its temperature increase, a predetermined thermal model exhibits gradual performance degradation through time. In this paper, a real-time multi-step prediction method is proposed to model the thermal effect of bioimplants. Compared with other human body thermal modeling techniques like FDTD and Fourier transform based methods, the method proposed in this paper is much faster and the computational cost is much lower, so it can be implemented online. The developed method can adaptively update the thermal model based on the real time temperature measurement of the tissue having direct contact with the bioimplant, which is easily available by integrating a temperature sensor into the implant, and accurately predict the future tissue temperature under various operation conditions. The effectiveness of the developed method is demonstrated with a battery powered neural prosthesis, which stimulate the neural tissues with Utah Electrode Array (UEA). The thermal effect of UEA is emulated with a COMSOL model, which has been experimentally validated in previous literatures
Research on Weak Signal Feature Extraction Method Based on Double Coupled Duffing Oscillator Stochastic Resonance
A weak signal feature extraction method is proposed based on double coupled Duffing oscillator stochastic resonance (SR). In this method, the doubly coupled stochastic resonance system consists of two Duffing oscillators. Stochastic resonance is generated in the coupled stochastic resonance system by the synergy of input weak signal and noise, so that signal to noise ratio (SNR) of output signal is enhanced. The influences of different parameters on SR are researched by SNR. The scale transformation method is used to realize the detection of large frequency signals. At first, the large frequency is compressed into a small frequency to satisfy the stochastic resonance through the twice sampling frequency. Finally, the feature extraction of weak fault signal is realized through the scale recovery. The analysis results of simulation signal and example signal of rolling element bearing show that this method can effectively extract the fault feature information of weak signal. The experimental results show that this method is more effective for weak signal detection than the Duffing oscillator detection
Study on Feature Extraction Method of Fault Signal Based on Reinforcement Cascaded Multi-stable Stochastic Resonance System
The paper presents a new method of fault signal feature extraction based on reinforcement cascaded multi-stable stochastic resonance (SR) system and it can extract signal fault feature from heavy background noise. The multi-stable model can further increase the noise utilization to achieve better detection effects of SR for weak signals than the bistable stochastic resonance system. On the basis of cascaded multi-stable stochastic resonance theory, the reinforcement characteristic of stochastic resonance induced by adding the second driven periodic signal is studied. Adding the second driving signals to the first and the second cascaded system, when the time scale is matched with the stochastic fluctuations, the phenomenon of frequency absorption happens and the effect of stochastic resonance in multi-stable system is reinforced. At last, an example of rolling bearing fault signal confirms that this method can efficiently extract weak fault signal feature, and has a good prospect in signal detection fields
A Correlation Study of Vibration and Noise Signals by Analyzing Its Responses for Monitoring of High-Speed Trains
In high-speed trains, vibration has long been an inevitable problem caused by many ambient effects, including wheel polygonization, rail defects, imperfect suspension systems, aerodynamic instabilities, meteorological conditions, and topographical variations, which can disturb the dynamic response and interior noise levels. These factors can be either correlated with or independent of each other. Excessive vibration levels of high-speed trains can lead to a series of problems related to ride comfort, train stability and track deterioration. Naturally, vibration and noise are interacted with each other, in which noise can be transmitted through air-borne and structure-borne paths to the interior of high-speed trains. The variation of noise levels is very sensitive as highspeed trains pass through various topographical terrains. Besides, aerodynamic noise becomes dominant as the speed of trains increases or trains travel through tunnels. Tracing the source that generates anomaly noise responses can give an important clue for monitoring operational safety and detecting adverse status. Pilot studies for the online monitoring of noise in the bogie of a high-speed train running on a railway in China have been conducted. The present study aims to investigate the relationship between ambient vibration induced by the rail-wheel interaction and noise measured from the bogie. The dynamic behaviour of high-speed trains is analysed in relation to the topographical variations (e.g., tunnels, viaducts, and plain area). Noise levels of the bogie are also investigated among various types of environmental conditions. Furthermore, the frequency response function is adopted to explore the correlation between the detected noise and ambient vibration using those on-board monitoring data
A Probabilistic Approach for Fault Detection of Railway Suspensions
Vehicle dynamics and safety against derailment are directly influenced by the primary and secondary suspension of a railway vehicle. During the operation faults of components like broken springs or dampers can occur. To prevent a complete system failure, the early detection of faults in the suspension of trains is thus of high importance. For the application of a vibration-based fault detection system several acceleration sensors can be mounted on the frame of the bogie. The signals of these sensors are collected during operation and are available for the application of online monitoring methods. In this publication, we present a probabilistic method to distinguish the faulty and the fault-free cases. The utilization of multiple sensor signals from one bogie for the fault detection and diagnosis yields a performance increase. This is conducted by the application of a subspace-based system identification algorithm. Afterwards, identified mode-shapes, damping factors and eigenfrequencies are considered in a probabilistic approach to distinguish the different failure causes. In this methodically new approach probability density plots are used to describe the most likely values of the eigenfrequencies and other characteristic parameters in the faultfree and faulty case. Comparing new data to the density plots determined in a previous training phase allows to assess if a failure has occurred or is likely to occur and what type of fault is the most likely one.
A Time Series Decomposition Method For Heteroskedastic Data In Structural Health Monitoring
Heteroskedasticity is often observed in long-term monitoring data in the context of SHM, which is normally induced by the seasonal variations of the ambient environment. In the effort to project out the environmental and operational variations, cointegration, a method originating in econometrics, has been successfully employed in various SHM studies. This paper will explore a possible enhanced approach to cointegration, applicable to heteroskedastic data. The fact that the variance of heteroskedastic data is constantly changing has a significant negative impact on conventional damage detection algorithms, making it difficult to calculate accurate confidence intervals. Thus, in the current paper, an exponential smoothing method is presented to explore and deal with the complex seasonal patterns in time series. More specifically, in this framework, a seasonally-corrupted time series can be decomposed into three components, namely, level, seasonal and residual terms. Subsequently, the series purged of seasonality will be fed into a cointegration analysis, in order to produce a more stationary residual series (damage indicator series). A case study of SHM of the NPL Bridge is demonstrated with results.
Damage Assessment of High-speed EMU Train Bolsters Via PZT Sensor Network-based SHM Technology
Structural health monitoring (SHM) technology is a popular method for determining the integrity of structures. A case study of using SHM technology to monitor the structural integrity of high-speed EMU train bolsters is performed. SMART Layers™, which are a kind of PZT (lead zirconate titanate) sensor networks, are employed to collect sensor data based on the propagation of guided waves. The sensor network works with hardware, cables, and diagnostic computer with software installed can become a complete SHM system. In this paper, the work of wave propagation test has been conducted to confirm many fundamental parameters, such as wave propagation distance, driving frequency and critical damage size. The customized SMART Layers™ were manufactured and installed on a bolster structure to evaluate the functions of SHM technology. The bench test on a full-scale bolster component has been conducted in the laboratory of CRRC. Experimental results show that the system integrated with SMART Layers™, hardware and software can accurately detect, locate and evaluate the size of cracks. However, there are some deficiency results from the bench tests requiring improvement in the near future, such as to improve the density of SMART Layers™ to defect a smaller crack, to upgrade the software and to satisfy the baseline data number in order to improve the performance of the system.
Damage Detection of Train-Track System with Extreme Function Theory
This paper focuses on detecting the damage of train–track system with the extreme function theory, which is a probabilistic method for damage detection. The train–track system adopted takes the coupling of vehicle and track into account. To detect the damage of the train–track system, the function of vehicle response with respect to the marching velocity of the train is used. The function can be represented by several discrete multi-dimensional data points. The extreme function theory deals with novelty as the distinction of the distribution tails of the conditional probability density. Normal model is constructed from the functions collected from vehicle in good service. A case study of train-track system with damage from the second suspension stiffness verifies the method well.
Design and Application of Bridge Deck Alignment Monitoring and Early-Warning System for Beipan River Bridge of Shanghai- Kunming Passenger Dedicated Railway Line
Track regularity is the guarantee of traffic safety and comfort performance of high speed railway. Inevitably, bridges always suffer from uneven settlement due to the effects of train loads, temperature, concrete creep and foundation deformation, etc., which bring the rise of maintenance cost and probably higher safety risks. Regarding the necessity of bridge deck alignment monitoring, the advantages/disadvantages and serviceability of several conventional deformation monitoring methods were discussed in present work based on Beipan River bridge of Hukun Passenger Dedicated Railway Line, which is actually ranked as the largest main span arch bridge of high speed railway all over the world. A design scheme of bridge deck alignment monitoring and early-warning system during operation period was raised. The layout scheme of system measurement points, system power supply, accuracy requirement, data transmission, operational safety assessment and early-warning were specifically discussed according to the structural characteristics of Beipan River bridge. To verify the reliability of monitoring data, a comparison work between automatic monitoring data and manual monitoring data was conducted. The practical application cases indicated that the monitoring system has the characteristics of high precision and low cost and it can assure the running safety of operation trains and provide scientific evidences for decision-making of daily maintenance and management work (e.g. when is the best time to adjust the alignment of track and how much of the accurate quantities should be adjusted). In addition, the monitoring data can also be useful for grasping the characteristics and evolution law of shrinkage and creep of this special kind of bridge, and providing strong and useful technical supports during designing and researching processes.
Detection of Performance Deterioration of High-speed Train Wheels Based on Bayesian Dynamic Model
This paper presents a novel technique, in the context of Bayesian dynamic linear model (BDLM) and Bayesian forecasting, for detecting the performance deterioration of high-speed train wheels using online monitoring data of strain acquired from in-service train bogies. The BDLM is a tool for time series analysis and Bayesian forecasting enables to calculate one-step ahead forecast distribution. The change detection is carried out by checking the current observation against the current model (forecast distribution generated by the BDLM for current instant) as well as against an alternative model (whose mean value is shifted by a prescribed offset). The detection rule is that if the alternative model better fits the actual observation, a potential change is alarmed. To further determine whether the current observation is an outlier or the beginning of a change, a specific logic is developed by introducing the Bayes factors and cumulative Bayes factors. The proposed method is demonstrated by using the monitoring data acquired from an in-service high-speed train under different wheel quality conditions
Fiber Bragg Grating Sensing System for Detection of Laser-Induced Ultrasonics in Application to Railway
This paper presents a new approach for detecting ultrasonic generated by a laser in the confined ablation regime, utilizing an adaptive fiber Bragg gratin (FBG) sensing system. A nanosecond pulsed laser is used for generating ultrasonic wave in aluminum specimen with water-confined layer. Experimental measurements demonstrate that the amplitudes of Rayleigh wave in the confined ablation regime are unexpectedly 8 times larger than the ones in direct ablation regime when the incident laser intensity is about 0.12×109 W/cm2. It is shown that confinement of the surface with a waterconfined layer provides an effective method of enhancing amplitude of laser-induced ultrasonic waves in the target material about an order of magnitude. These results indicate that the possibility of applying the laser-based FBG ultrasonic sensing system in nondestructive testing (NDT) and structure health monitoring (SHM) within special structural materials of high-speed train
High-speed Non-contact Passive-only Ultrasonic Inspection of Rails from Deconvolutions of Wheel-generated Noise
Rail internal defects have been one of the major causes of train derailments in the U.S.. While the conventional ultrasound wheel probe has been widely implemented for rail track inspection, it suffers from the drawback of limited testing speed (25 mph at most). This paper presents a radically new approach for ultrasonic rail inspection that utilizes non-contact receivers and no active transmitters. The transfer function between two points of the rail is reconstructed by deconvolutions of multiple pairs of receivers that sense the acoustics naturally excited in the rail by the running wheels. The deconvolution process eliminates the random effect of the excitation to reconstruct a stable acoustic transfer function of the rail. This approach lends itself to extremely high testing speeds (as fast as the running train, e.g. 100 mph and above), that would enable the implementation of “smartâ€ technology on trains running at normal operational speeds. A prototype based on this passive-only inspection idea has been constructed and tested with the DOTX216 testing vehicle of the Federal Railroad Administration at the Transportation Technology Center (TTC) in Pueblo, CO in September 2016. Test runs were made at various speeds from 25 mph to 80 mph (the maximum speed allowed on the test track). The results show the feasibility of stable reconstruction of the transfer function from the random wheel excitation, as well as the detection of joints and welds present in the track. Some tests were also conducted on TTC Defect Farm showing the potential for defect defection.
Key Technologies and Experimental System of High-speed Train Tracking Monitoring
The researches of high-speed train service performance need to be more rely on realtime tracking detection of train service performance, using the theory of modern signal processing, intelligent information processing method and the dynamic theory to intercross and fusion, and inverting the service performance of vehicle parts, systems and the whole vehicle based on the detection and monitoring of the physical characteristics, so that to be more fit the actual demand to the guidance of train safety application, scientific maintenance and optimal design. The objective of the article is to establish the system of the on-line field tests for the high-speed train. The article first introduce the test method and system as well as the field conditions including the main parameters of the test train, especially the running gear system which is the heart of the vehicle. Based on the test data, the article presents the vibration behaviors of car body, bogie frame and axle box as well as the lateral hunting stability of the train operating at the speed of 350 km/h, and the evolution of the train dynamic performance under long term operation condition. The partial monitoring results show that the vibration accelerations of vehicle components are stable and in a low level when the train runs on a non-ballasted track at the speed of 350 km/h, indicating that both the ride comfort and the running stability are acceptable. Through wheel re-profiling, it is possible to effectively reduce the vibration of the axle box, the bogie frame and the car body, and thus the vehicle dynamic performance can be kept in an excellent condition in a long period. The future research topics are proposed in the article terminal.
Monitoring and Analysis of Acoustic Characteristics of Interior Noise of a High-speed Train Running in Low Temperature Environment
In this study, the interior noise at the end of a high-speed train coach were monitored and analysed under a working condition of 250 km/h in an extreme low temperature environment (between -28 ℃ and -30 ℃). Meanwhile, the vibration levels of the floor, panel, ceiling and bogie systems were also measured. Subsequently, these monitoring data was used to conduct an interior cavity acoustic modal simulation. The simulation results confirmed the characteristics of the noise distribution and the noise generation mechanism. Accordingly, countermeasures against this kind of interior noise were proposed and then it is implemented to reduce the interior noise at the end of the high-speed train coach. The effectiveness of the method in noise reduction was verified. Based on the verification results, a new type of floor structure using a metal damper was designed to attenuate vibration energy at 110Hz. The purpose is to effectively control abnormal noise caused by the acoustic-structure coupling. The present results indicate that the noise and vibration of the high-speed train using a new type of floor structure is significantly lower than the original train without this damping structure. This design will efficiently improve the low frequency vibration as trains pass through sleepers.
Monitoring and Analysis of Train-Track- Bridge Interaction Behavior: A Case Study of Train Puyouma Passing Through Tai’an Viaduct in Taiwan
In this paper, several measurement instruments, including thermal fiber Bragg grating (FBG) sensor, strain gauge, non-contact displacement measurement technique, and GPS, have been applied to the case when a titling train Puyouma moved through the Tai’an Viaduct in Taiwan. Using measured information, consisting of vibrations on train, rail, and bridge, strain, deformation, and vertical and horizontal wheel-track contact forces, could verify the performance and safety on the rails and bridge at different train speeds. The computed values on rail internal force, rail frame strength, superstructure stiffness, and derailment index were also used to calibrate the parameters used in an as-developed 3-D finite element simulation model. In future, this train-track-bridge interaction simulation model can effectively evaluate the safety and performance on a railway bridge.
Online Monitoring of Rail Damage Based on Wigner-Ville High Order Spectrum
According to the fracture mechanics, Rail damage acoustic emission (AE) signal is related to the rail material, structure, load and crack development. For the rail with fixed material, fixed structure and load fluctuating in a small range, the rail damage can be monitored on-line by monitoring the AE signal. The existing AE signal processing technology can't simultaneously achieve noise suppression and high resolution transform domain representation. By introducing Wiger-Ville higher-order spectrum, the high resolution time-frequency domain two-dimensional representation of Rail damage AE signal under strong background Gauss noise is obtained successfully. The feature extraction and clustering analysis of Rail damage AE signal are realized. Accurate identification of rail damage AE signal of rail fracture, spalling of rail bottom, spalling of rail head, wheel/rail impact, friction of wheel flange, switching of pneumatic turnout, temperature force release is achieved. Theoretical analysis, experimental data (more than 3000), field data (more than 3 million) have proved the effectiveness and reliability of the method.
Operational Early Warning Analysis of High- Speed Railway Bridge Structure Based on Sliding Window Technique and Data Driven Stochastic Subspace Identification Algorithm
Taking the seismic shaking table model test of a typical railway cable-stayed bridge as the research background, the sliding window technique and data-driven stochastic subspace identification (Data-Driven SSI) algorithm were employed to study the early warning for operation state of high-speed railway bridge structure. To achieve the real-time tracking and recognition of the modal parameters of high-speed railway bridge in time domain, the sliding window technique was introduced to data-driven stochastic subspace identification algorithm, and a sliding-window stochastic subspace identification method was proposed. Subsequently, based on the results of sliding window stochastic subspace identification algorithm and combining the state evaluation indices of high-speed railway bridge in various countries, the operational early warning method of high-speed railway bridge based on frequency change rate was proposed, which realized the real-time state evaluation for railway bridge. By using the testing data of the model test in different testing conditions, the real-time evaluation and early warning analysis of the state of the model bridge corresponding to different testing conditions were carried out. And then the real-time early warning results were compared to the actual cracking and damage situation respectively, the results show the correctness of this method and that the method can be applied to the operational early warning analysis of high-speed railway bridge
Predictive Maintenance of Ground Power Supply System for Tramways –Anomaly Detection and Localization
Health assessment of railway infrastructure is challenging and expensive, mainly because of the extensive length of the railway network and also due to the wide variety of defects that could affect the tracks. Early warning systems that minimize the disruption of the network can have a beneficial impact on the reliability and on the availability of the infrastructure. In the context of its predictive maintenance program, Alstom aims at offering Prognostics and Health Management tools to detect and diagnose different types of degradations on the track infrastructure and to prevent them. The Aesthetic Power Supply (APS) system aims at providing power to a tramway using a third rail located on the ground. Two current collector shoes are mounted on the train to collect the power. The APS rail is composed by consecutive powered sections and isolated sections. Infrastructure degradation or vertical misalignment of rails in turnout, for example, may induce shocks on the current collector shoes leading to the breakage of the collector shoe itself and may also lead to a service disruption. By detecting and localizing shocks on the APS rail, it is possible to precisely act on the APS infrastructure to fix it. The study presented in this paper is based on acceleration data measured at the current collector shoe. Data are collected during a mission over the entire APS network using an acquisition system with a GPS incorporated. Subsequently, the recorded data are analyzed to detect abnormal vibration levels for different ranges and to locate the abnormal vibration on the APS infrastructure using GPS information
Signal Processing and Analysis Techniques Used in Monitoring the Integrity of Rail Vehicle Components
Structural health monitoring (SHM) technology is planned to be used in monitoring rail vehicle components, such as high-stress areas of bolsters, carbodies and bogies, etc. Many advanced techniques are studied for assisting the SHM technology in realizing functions of designing sensor layout, processing signal, and finding, locating and evaluating damages. Based on the geometry and the operating features of rail vehicles, some existing measures, such as calculation of probability of detection at SHM sensor network, extraction of first arrival windows from received signals, calculation of damage indices and compensation of signal changes resulting from environmental factors are employed in the studies. In this paper, the following four aspects are reported: first, description of the current status and challenge of maintaining rail vehicle components; second, introduction of operating features of rail vehicles; third, introduction of SHM technology and some related leading methodologies that are available for monitoring the integrity of rail vehicle components; fourth, based on the first three aspects, a preliminary study has been conducted and the recommendation for future studies is discussed
Rail Contact Stress Real-time Monitoring using Ultrasound Reflectometry
The rail stress is vital to lifespan, safety of rail tracks and riding comfort, among which the wheel-rail contact stress is a determining factor to wear, damage, cracks and failure of both wheel and rail. The wheel-rail contact system has been investigated and simulated for years, and a variety of methods have been developed for stress determination, but there is still no robust measuring technique for accurate real-time online stress characterization. The ultrasonic reflectometry technique has been proven successfully in characterizing various static machine-element contacts. The general process is firstly, an ultrasonic wave is emitted through a material that is either fully or partially reflected at an interface. The reflected waveform is then measured and information about the contact can be extracted from the A-Scan. The contact interface mechanism is modelled using a quasi-static spring approach. In this paper, an ultrasound reflectometry measuring method was introduced for rail contact stresses determination, and a set of ultrasonic tools were developed in order to take measurements under various situations. Wheel-rail contact stresses were profoundly investigated. Static contact stresses of normal wheel-rail contact under different normal loads were measured using ultrasonic focusing probe, high resolution images of contact stress distribution were obtained from measurements. Dynamic wheel-rail contact stresses were characterized using array of ultrasonic elements. A pitch-catch measuring technique was introduced for dynamic contact stress measurements, and specific ultrasonic arrays were designed and made for different mounting places. Measurements were taken under different normal loads, and stress distribution image were plotted from the measurements.
Research and Application of Random Vibration Fatigue Assessment System for Welded Structures in Railway Vehicle
With the increasing speed of railway vehicle in China, the problem of fatigue failure in welded structures is becoming more and more serious. Because the frequency range of the external excitation load is wider, the structures are more likely to yield modal vibration, which will accelerates fatigue failure process of welded structures. Therefore, how to make it possible to take the effects of load frequency distribution on fatigue life into consideration when evaluating welded structures is a subject worth studying. Firstly, a new fatigue life prediction method of welded structures is proposed in this paper by the combination of the random vibration theory and the Structural Stress Method in ASME 2007 standard which is the most reliable fatigue life prediction method of welded structures so far. Fatigue life of welded structures can be calculated under frequency domain in this new method which is called Frequency Domain Structural Stress Method. Then, a random vibration fatigue evaluation system of railway vehicle welded structures based on this new method was built through C++ language with the cooperation of commercial software. This system make it possible to predict the fatigue life of welded structures at the product design stage of railway vehicle under random loads from measured or standards(can be force, displacement, velocity and acceleration data). An engineering application case of this system on the random vibration fatigue assessment of a car body lifting structure in an urban railway vehicle was carried out to verify the effectiveness of this system. This system can not only be used in the railway industry, but also in other industries.
Structural Health Monitoring of High-speed Railways using Ultrasonic Guided Waves
This paper presents a damage detection strategy for high-speed railways using piezoelectric active sensors. Multimodal ultrasonic guided waves generated by a piezoelectric transmitter propagate along the rail track, undergo dispersion, interact with the damage zone, and are finally picked up by the sensors. First, numerical investigations are carried out to understand the guided wave features and their interaction mechanism with typical damage scenarios in the railways. The modal analysis of a finite element scheme with Bloch-Floquet condition is conducted to obtain the dispersion characteristics and the mode shapes of the rail track guided waves. Optimum wave generation location and frequency were explored using a small-size local coupled field finite element model. Further, a Local Interaction Simulation Approach (LISA) model was developed to achieve efficient simulation of elastic wave propagation in railway structures. The LISA procedure was coded using the Compute Unified Device Architecture (CUDA), which enables the highly parallelized computing on powerful Graphics Processing Units (GPUs). This transient dynamic analysis reveals the influence of rail track features and damage signature on the sensing signals. Finally, full-scale experiments on a BS 90A rail track with embedded piezoelectric sensors are carried out to compare with the numerical investigations. This study shows that the active sensing system possess promising potential for the in-situ health monitoring of railway structures
Study on Vibration Characteristics Analysis and Characterization of Multiple Failure Modes of High-speed Train Axle Bearing
This paper focuses on analysing vibration signals from the axle bearings of highspeed trains to quantitatively characterize the bearings’ operating conditions. A novel characterization method for analyzing the axle-box vibration signature based on the EEMD shape function is presented. A wheel-set running test bench is used to obtain the different vibration signals of an axle bearing with multiple fault statuses. The main IMF of measured signals is selected to establish the shape function. The resulting steadiness index contains well-defined dividing value intervals with no overlap between multiple axle bearing states. Based on the results obtained, the proposed method provides an effective and reliable condition monitoring procedure and a reference threshold for axle bearing online monitoring strategies and alarm mechanisms in real-world applications
Damage Diagnostic Techniques and Experimental Research of High-speed EMU Aluminum Carbody Bolster Based on Lamb Waves
Aluminum carbody bolster is a key bearing structure of a high-speed EMU. It may suffer crack and corrosion damages from train operations. Those damages are detectable from guided wave-based ultrasonic technologies. In this paper, the case study of crack damage detection is performed by using a Lamb wave-based structural health monitoring (SHM) system in a carbody bolster. By applying the Hilbert transform technique to abstract the Lamb wave signal features, a damage index (DI) calculation method is proposed. In order to obtain an optimal DI result, the environmental parameter such as temperature has been studied and compensated in the diagnostics. A simple damage diagnostic technique combing with Hilbert Transform-based DI calculation, image method for damage localization is developed. An experimental study has been carried out in the laboratory by using a small actuator-sensor network bonded on the surface of a bolster structure. The structure is drilled with holes and saw cuts at the edge of holes for damage simulation. A calibration technique of crack size versus damage index was also presented.
Field Test and Dynamic Characteristics Analysis of High-Speed Maglev Guideway Girder
High-speed maglev, as the fastest transportation on the ground, may become the most promising long-distance transportation choice in the future. The dynamic characteristics of the girder, which is the main supporting member, is very important to the design and health monitoring of the maglev transportation system. This paper presents the field measurement and numerical analysis of the dynamic behavior of the guideway girders in Shanghai high-speed maglev test line. An output-only time-domain system identification method namely, the Natural Excitation Technique (NExT) coupled with the Eigensystem Realization Algorithm (ERA) was used in this study. The dynamic responses of the girders are presented and analyzed in both time and frequency domains. The results show that the NExT-ERA method can well predict the inherent dynamic properties of the girders.
First Field Results of Guided Wave Tomography for Continuous Monitoring of Corrosion and Erosion Damage in Pipelines
The effective life management of pipelines in the Oil and Gas industry requires monitoring techniques that can probe an extended section of a pipe and can sample its state with relatively high frequency so as to implement corrosion mitigation in a timely fashion. Due to the typically low corrosion rates, 1 mm/yr or less, the monitoring technique must be sensitive to wall-thickness changes that are in the order of a few tens of micrometers. Ultrasonic guided wave tomography (GWT) with permanently installed sensors offers an attractive compromise between sensitivity and area coverage by combining the long range propagation characteristics of guided ultrasonic waves with the principles of model-based inversion. Here, the pipe section to be monitored is delimited by two ring arrays of ultrasonic transducers that encircle the pipe and measure guided wave signals after traveling inside the pipe wall from one array to the other. The signals are interpreted by advanced GWT algorithms to form a wall thickness map of the entire pipe section. In this paper we review the principle of operation of GWT, discuss its performance, and present the first long term field results obtained for a pipe in a deepwater rig in the Gulf of Mexico. The field results show that GWT based on EMAT transduction provides highly stable wall thickness estimations even in the presence of multiphase flow inside the pipe undergoing a wide range of pressure and temperature fluctuations.
Inverse Calculation of Displacements in CNF/PU from EIT-Imaged Conductivity Changes
Composite materials modified with carbon nanofillers have been thoroughly studied for structural health monitoring (SHM) and damage detection applications because they are piezoresistive and therefore self-sensing. That is, mechanical effects such as strain and damage collocate with conductivity changes within the material. The visualization of strain or damage-induced conductivity changes can then be leveraged for damage identification. To this end, electrical impedance tomography (EIT), has also received considerable attention for SHM because it can non-invasively image spatiallydistributed conductivity changes. Despite the potential of piezoresistivity and EIT for SHM, this approach has an important limitation. EIT can only deduce conductivity changes. Conductivity, however, is not a structurally relevant parameter. From a SHM perspective, it would be much more useful to know the underlying mechanical state of the structure that gives rise to the observed conductivity changes. To achieve this, a novel piezoresistive inversion process is herein presented. This process endeavors to inversely determine the underlying displacement field of a piezoresistive material that results in an observed conductivity change as determined via EIT. The accuracy of this process is experimentally tested on a carbon nanofiber (CNF)/polyurethane (PU) nanocomposite. These preliminary results demonstrate that it is indeed possible to inversely determine the mechanical state of a body from conductivity data.
Noncontact Epoxy Curing Monitoring and Subsurface Damage Detection
Epoxies are an integral element of fiber-reinforced polymer composites, which are increasingly being used in various aerospace, automotive, civil, and naval structures. In addition, nanofillers, such as carbon nanotubes (CNT), are infused in epoxy resins to enhance their electromechanical (sensing) and thermal properties, among others. For both cases, and as innovative epoxy-based composites are being developed, monitoring and understanding their curing process is important for establishing manufacturing guidelines. Moreover, the ability to inspect for and to detect damage (e.g., defects created during manufacturing) is crucial for ensuring product quality and performance. The main objective of this study was to develop a noncontact technique for monitoring curing of epoxy and for detecting defects (or damage) in pristine and CNT-enhanced epoxy resins. First, electrical capacitance tomography (ECT) was employed for determining, in a noncontact fashion, the permittivity distribution of materials such as epoxy resins. Second, ECT was used for monitoring curing of pristine and CNTenhanced epoxy specimens. The results showed that reconstructed permittivity values of these specimens changed and correlated with curing times. Last, different sizes of damage were introduced to epoxy specimens. ECT was validated for its ability to detect the locations and sizes of these damage features
Preliminary Findings on the Size Dependence of Sensing Skin Damage Detection Resolution
Recently, there has been a growing interest in the development of large-area sensors for Structural Health Monitoring (SHM). These sensors have a wide range of applications in monitoring civil and mechanical structures. A class of these sensors that are commonly referred to as sensing skin, consist of a thin layer of electrically conductive materials, e.g., electrically-conductive paints or films. These sensors can be installed on the surface of the structure or embedded within materials. Damage to the structure results in a local conductivity change of the sensing skin. The spatial and temporal distribution of conductivity of the sensing skin is reconstructed using Electrical Resistance Tomography (ERT), enabling characterization of the damage. In small sizes, in the order of a several inches, the sensing skin technology has been very successful in localizations and quantification of damage in terms of conductivity change. There remains the question, however, as whether the performance of sensing skin is affected by increasing its size. This paper reports the preliminary results of testing the feasibility of a large scale (1.22 m 1.22 m) for damage detection. Further, we study computationally and experimentally the damage detection resolution of the large scale sensing skin
Observing the Fracture Behavior of a Center Crack Via Electrical Impedance Tomography using Inkjet-printed Carbon Nanotube Thin Films
Optimized lightweight design materials such as fiber-reinforced polymer composites (FRPs) allow aerospace and automotive industries to build robust structures with reduced self-weight, attaining improved fuel-efficiency with less strength loss. These structures require rigorous assessment to prevent catastrophic structural failure. Current development in tomographic methods offers opportunity to perform online damage assessment of a structure without installing heavy instrument over a target region. This paper demonstrates the spatial sensing capability of an inkjet-printed carbon nanotube (CNT) thin film upon a crack’s fracture behavior via electrical impedance tomography (EIT). A CNT-thin-film-printed coupon with a center crack was tensile loaded and evaluated under three loading cases. The printed thin film served as both a spatial damage sensor and a speckled pattern for digital image correlation (DIC) analysis. The EIT results, which were compared to the DIC measurements of the planar strain distribution developed over the thin film, showed that the sensor may identify crack severity and plastic zones formed at crack tips.
A Fully Integrated Health Monitoring Smart Sensor
The Health Monitoring Smart Sensor (HMSS) is a fully integrated, advanced structural health monitoring platform, currently on test flights monitoring one of the Airbus A380 engines, able to detect and report to the aircraft computer a variety of degradations. It provides a main plug for the aircraft power and an auxiliary plug for a thermoelectrical power harvester. The firmware switches automatically between the two power sources by monitoring the harvester charge level. The aircraft supplies status signals indicating the flight mode (ground/flight) and the engine mode (on/off). A RS422 link provides data to the aircraft computer when the aircraft has landed and the engine has been switched off. The electronics protect the power sources and communication lines in case the plane is struck by a lightning. Environmental sensors (temperature, pressure, relative humidity) monitor ambient conditions while several thermocouples monitor temperature at select engine areas. Several coil sensors are utilized to detect cracks by performing electrical continuity checks. Moreover, by measuring the coil inductance, any sensor detachment from the structure is detected. The sampling frequency per sensor and flight mode is parametrical. Independent power domains per sensor/component, activated only when needed, and the microcontroller low power modes result to ultra low power consumption
Challenges in Meeting the Framework for SHM System Certification and Compliance
Monitoring the continued health of aircraft subsystems and identifying problems before they affect airworthiness has been a long-term goal of the aviation industry. Towards this end, Structural Health Monitoring (SHM) systems that could be used for the in-service monitoring of the health of new and existing aircraft structures are being matured. This paper will go step-by-step into the various challenges associated with meeting preliminary certification compliance requirements. A 5-year program completed under funding from the Federal Aviation Administration will be used as a guide to lead the reader through the various certification compliance steps The objectives of this program were to (1) develop, validate, and demonstrate Smart Patch System (SPS) technologies including advanced software, algorithms, and methodologies for structures and dynamic components to (a) detect fatigue cracks and damages before exceeding critical threshold or incipient failure and (b) characterize fatigue cracks and damages, and (2) develop preliminary certification compliance requirements and a framework for certification of an SPS that would be integrated into a Health and Usage Monitoring System (HUMS) for the fatigue crack monitoring of commercial rotorcraft structures per the guidelines provided in FAA Advisory Circular AC 29-2C, Section MG-15. This paper will use this program as a guide to discuss certification compliance challenges and recent work done to meet them. It presents the details on the tasks that were conducted towards the development, validation, and potential certification of the SPS and other similar SHM systems for specific structural applications.
Convergence of Multiple Statistical Methods for Calculating the Probability of Detection from SHM Sensor Networks
The use of in-situ sensors for real-time health monitoring of a wide array of civil structures can be a viable option to overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. The maturity of Structural Health Monitoring (SHM) sensors has evolved to the point where many networks have demonstrated sensitivities that meet or exceed current damage detection requirements. As a result, there is a growing need for well-defined methods to statistically quantify the performance of sensors and sensor networks. Statistical methods can be applied to laboratory and flight test data to derive Probability of Detection (POD) values for SHM sensors in a fashion that agrees with current nondestructive inspection (NDI) validation requirements. However, while there are many agreed-upon procedures for quantifying the performance of NDI techniques, there are no guidelines for assessing SHM systems. While the intended function of the SHM and NDI systems may be very similar, there are distinct differences in the parameters that affect their performance and differences in their implementation that require special consideration. Factors that affect SHM sensitivity include flaw size, shape, orientation and location relative to the sensors, operational and environmental variables and issues related to the presence of multiple flaws within a sensor network. The FAA Airworthiness Assurance NDI Validation Center (AANC) at Sandia Labs, in conjunction with the FAA WJH Technical Center, has conducted a series of SHM validation and certification programs aimed at establishing the overall viability of SHM systems and producing appropriate precedents and guidelines for the safe adoption of SHM solutions for aircraft maintenance. This paper will present the use of several different statistical methods, some of them adapted from NDI performance assessments and some proposed to address the unique nature of damage detection via SHM systems, and discuss how they can converge to produce a confident quantification of SHM performance. Comparisons of hit-miss, a versus Èƒ, and One Sided Tolerance Intervals will provide valuable insights into how the characteristics of the collected SHM data affect the formulation of that system’s POD curve. Similarities between NDI and SHM assessments will be highlighted in order to provide a foundation in traditional flaw detection performance measures. In addition, considerations of the controlling factors to be considered when collecting SHM response data will be discussed
Defect Detection using Electro-Mechanical Impedance Sensing and Optical Fiber Bragg Gratings – a Comparison
Electromechanical impedance (EMI) sensing and strain sensing via optical fiber Bragg grating (OFBG) sensors are both widely researched structural health monitoring (SHM) techniques, but are based on very different approaches. The EMI approach monitors changes in the frequency of high order structural vibration modes that are generally not observable using conventional vibration testing. SHM using OFBG sensors is performed either dynamically, monitoring vibration, or statically, monitoring defect-induced perturbations in the strain field. While the EMI approach, being a semi-active approach, is probably the more versatile and powerful, the aerospace community has for some time looked more favorably, broadly speaking, on OFBG sensors due to the inherent advantages of optical fiber sensors, such as lightweight, multiplexing ability, amenability to be embedded in composites, etc. In other words, the choice of method to be explored was often based on system considerations rather than inherent ability to detect damage. With recent technical developments in microelectronics, including ultra-low power devices, decreased component size and weight, and wireless sensor network protocols, the EMI method can now be considered in applications that were previously not considered possible, and engineers are thus more able to decide on a SHM method based on damage detection ability alone. It is thus important to clearly understand how well the two methods compare in their ability to detect damage, and one way of getting at this understanding is to apply both methods to the same problem. Thus, in this effort both methods are applied to the problem of detecting crack growth in a metal plate. Specifically, the defect to be detected consists of an edge slit in a metal plate, with multiple fasteners bridging the slit. Thus, loosening the fasteners is analogous to increasing the size of the slit. Macro-fiber composite (MFC) piezo-electric sensors were used to observe the EMI signature, and an optical fiber Bragg grating sensor was used to record dynamic strain during natural vibration of the metal plate.
Long Term Sensor Malfunction Detection and Data Regeneration using Autoregressive Time Series Models
Structural Health Monitoring (SHM) seeks to assess the integrity of a structure using installed sensors and a variety of structural and data analyses. SHM stands poised as a partial solution to maintain the failing infrastructure in America, but has not yet seen widespread implementation. One factor restraining SHM from universal implementation and prominence is a lack of reliability in sensing and monitoring as a whole. In many cases sensor readings can drift further and further away from the true measurement sought, or even fail altogether. This paper presents one method for the detection of drifting or failing sensors, based on a series of autoregressive (AR) time series models. These numerical models are trained on healthy sensor data, usually from the infancy of the structure or sensor use, and then the distributions of the residuals, or model error, is compared for healthy and unknown time periods on the structure. In cases of failed sensors, a multivariate AR model relating nearby, healthy sensors, is trained on early data before failure and then used to regenerate later time periods of sensor failure. This data regeneration will never perfectly replace the failed sensor, but still provides with replacement values that are sufficiently accurate so they can be meaningfully used in data analysis. These methods are validated on data collected from the Streicker Bridge, on campus at Princeton University.
Measuring Data Driven Fleet Management Benefits through Post Implementation Assessment
Highly constrained Department of Defense budgets compel the U.S. Army to develop and employ a suite of holistic Data Driven Fleet Management (DDFM) approaches. This collection of capabilities supports proposals for investments aimed at reducing and/or mitigating Operation and Sustainment (O&S) cost growth. As part its overarching DDFM strategy, the Army’s Aviation & Missile Command (AMCOM) invests in initiatives with the goal to improve reliability and reduce O&S cost, with no negative impact on the warfighter’s readiness. DDFM employs a Systems Engineering approach to collect specific data, enabling focused analysis using multiple relevant databases and methodologies. This approach develops actionable information for presentation to Army leaders to support their critical funding decisions. Army Aviation uses the Army Working Capital Fund (AWCF) to invest in approved initiatives. AWCF investments support innovative projects with the objective to more wisely sustain the world’s largest fleet of helicopters which includes the Apache, Black Hawk and Chinook platforms. As part of an update to AMCOM Logistics Center’s (ALC) manuscript published through the International Workshop on Structural Health Management (IWSHM) 2015, this edition for the 2017 workshop features updates regarding models the ALC utilizes to measure investment results. The prior published work spelled out how ALC employs its Post Implementation Assessment (PIA) model to measure benefits yielded when the Army authorizes retirement life extensions for aircraft components. PIA models validated how those extensions greatly enable the consumption of valuable remaining useful life (RUL), by increasing time-on-wing, therefore reducing sustainment cost. This updated manuscript features ALC’s PIA model, as specifically developed to measure investment results enabled by implementing redesigned components. Specifically, redesigns which occur to improve reliability, reduce cost and/or mitigate obsolescence.
Reference Damages for Verification of Probability of Detection with Guided Waves
For conventional ultrasonic testing the concept of reference damage for PoD is well-established and different reference damage types are in use, e.g. flat-bottom holes for ultrasonic testing. For guided waves in CFRP structures, these reference damages are barely useful, as they do not represent the characteristic changes in the guided wave signal, compared to real impact damage like delamination and debonding. Also most damage detection approaches based on guided waves depend on baseline signals, precluding some established reference damages like embedded Teflon layers. For that reason, a new class of reference damages needs to be defined. These reference damages shall be tailored for the application scenario including as well the applied analysis method. These reference damages may be attachable to the structure after baseline measurement and in best case be removable. They shall generate a comparable change in the features of the signal like real impact damage and, due to the complex design of experiment, be available in parallel in numerical simulation and in experiment.
Reliability Analysis of an Ultrasonic Guided Wave Based Structural Health Monitoring System for a Carbon Fibre Reinforced Thermoplastic Torsion-box
Certification is the key step towards the implementation of structural health monitoring (SHM) as part of condition based maintenance programmes of aircraft fleets. That can only be accomplished by demonstration of system performance in multiple scenarios and in a statistically relevant way. This paper describes a fully computational approach for reliability assessment of an ultrasonic guided wave (GW) based SHM system for a full-scale stiffened panel of a horizontal stabilizer torsion-box entirely made of carbon fibre reinforced thermoplastic. A pseudo-stochastic nature is attributed to the damage index values calculated from the finite-element signals, allowing hit-miss data to be generated. That data is in turn employed for the computation of probability of detection curves and relative operating characteristics. The results of this research show that detailed damage diagnostics in full-scale complex composite structures can be achieved by using ultrasonic GWs under the hotspot monitoring philosophy. Moreover, it is demonstrated that properly validated models of the monitoring environment can be used for reliability analysis of GW based SHM systems for full-scale complex composite structures, thereby reducing the need for extensive and costly experimental test campaigns, which in turn can contribute to an acceleration of the establishment of a certification framework for SHM systems.
Sensitivity Analysis and Uncertainty Evaluation in the Design of Structural Health Monitoring Systems
The complexity of uncertainty evaluation for autonomous SHM systems is compounded by the numerous conditions and variables for which the SHM system must demonstrate reliable and consistent performance. Performing all such tests on real structures is often infeasible. This work examines the feasibility of using a statistical experimental design (DOE) framework in the simulation environment in order to conduct a multi-factored sensitivity study to inform subsequent studies and, eventually, the validation of the SHM system. Specifically, we examined the simultaneous effect of varying sensor placement, flaw location, frequency, and sensor size on the performance of a known piezoelectric wafer active sensor (PWAS) in a simulation environment. Using fractional experimental designs, we demonstrate the replicability of using ½ the total number of experimental runs to identify the signal feature exhibiting the most sensitivity to system design factors and the factors exhibiting the greatest effect (greatest variability) on the SHM response. These factors varied depending on the damage index and feature used for analysis. Specifically, emphasis and care should be taken when designing an SHM system based upon the correlation coefficient and difference in peak amplitude with respect to the transmitter angle and receiver angle, in addition to frequency for the difference in peak amplitude. These factors accounted for between 90-98% of all the variability observed in the SHM response for these signal features. The results of this experiment demonstrate: 1) the usefulness of designed experiments for efficient uncertainty evaluation in SHM systems, 2) the sensitivity of damage indices to varying SHM factors, and 3) the ability of simulation experiments to effectively inform subsequent material and real-time experimentation. These initial results have provided the basis for more extensive demonstrations on complex simulation experiments in addition to laboratory and active material.
Simulation, Realization and Validation of Guided Wave SHM System Solutions for Aircraft Metallic Structural Repairs
Repairs in aircraft structures are an essential affair when the structural components may be subjected to either critical or non-critical damages during their service. Many of those repairs follow standardized designs while others turn out to be only solved as unique designs. To meet lightweight design criteria damage tolerance principles have to be applied. This is where SHM gains a specific interest and where optimised solutions have to be found. This describes the process of a SHM based approach for a metallic repair on a metallic aircraft fuselage where the approach is based on guided waves. The minimum requirement of any such SHM system to be developed is to reliably detect the given tolerable damage based on optimized actuator-sensor configurations. Unlike conventional ultrasonic transducers, which are free to move on the surface of the structure to be inspected and where data samples can be taken for a virtually unlimited combination of transducer positions, the piezo actuators/sensors an SHM-system consists of, are fixed regarding their position. Those positions have therefore to be clearly identified in advance with regard to an optimized location to capture the signals resulting from the damage. Finding that optimum location can reasonably only be achieved through simulation. In this paper a patched metallic repair solution is numerically modelled with regard to guided waves travelling through it for an undamaged condition and a condition with a tolerable damage respectively. The results for the two conditions are subtracted from each other and the resulting differential image is considered to indicate the locations where most of the damage resulting information can be ‘harvested’. This approach has been performed in simulation and validated experimentally
Stochastic Deviation Method of Calculating Nodal Positions of Existing Spatial Structures
According to the uncertainty and the inherent randomness of nodal positions of existing spatial structures, a stochastic deviation method (SDM) was proposed to calculate the nodal positions and to reckon the geometric shapes. Taking into account the random characteristics of spatial structures, sampling principles and an approach to calculating minimum sample size in the SDM were given. Deriving from probability and statistics theory, the procedure for inferring the probabilistic distributions of the nodal position deviations were proposed. The proposed SDM was adopted to reckon the geometric shape of a reticulated shell structure, and the nonlinear static stability analysis were carried out using the structural spatial position determined by the SDM. It was shown that the SDM can give more realistic results and can be used for the assessment of existing spatial structures.
Damage Quantification of Active Sensing Acousto-ultrasound-based SHM Based on a Multi-path Unit-cell Approach
Assessing the reliability of damage quantification is a critical and necessary process for the evaluation of Nondestructive Evaluation (NDE) or Structural Health Monitoring (SHM) techniques. When it comes to NDE techniques, appropriate processes have been matured and established for asserting the reliability of damage quantification. However, such techniques offer solutions which that can be applied offline, are time consuming, and oftentimes quite expensive. On the other hand, for the case of SHM-based methods, and although several probabilistic methods have been proposed in the state-of-the-art literature, the task of damage quantification of active sensing techniques poses significant challenges that need to be properly addressed. Specifically, a major safety concern in aerospace structures is related to fatigue induced cracks for which accurate and reliable quantification is a critical issue for achieving the design performance and ensuring the aircraft safety. The main challenges associated with the quantification of fatigue cracks originate from the acousto-ultrasonic response discrepancies in similar structural materials that are due to variations in operating and environmental conditions, sensor positioning, damage characteristics (crack location, orientation, and propagation), and the effectiveness of the employed diagnostic algorithms. Recent studies have shown that apart from the operating/environmental conditions, the main sources of variation and uncertainty in the acoustic response are related to the location of the sensors and the damage location and propagation patterns. Trying to address the latter, this study presents an active sensing damage quantification approach that is based on the use of multiple piezoelectric sensors and corresponding acousto-ultrasonic damage propagation paths. Multiple paths from a multiple-sensor configuration unit, referred to as unit-cell, are used along with an adaptive weighted averaging method to mitigate the effects of sensor positioning errors and/or uncertainties associated with crack size and orientation. Several coupon-level experiments have been conducted to validate the performance of the method and investigate the convergence of the accuracy for increasing number of sensors.
Accurate Structural Dynamic Response Monitoring of Multiple Structures using One CCD Camera and a Novel Targets Configuration
Structural impairments can be detected by monitoring and analyzing dynamic structural responses. In this paper, accurate dynamic deformations of multiple vibrating structures are estimated using one off-the-shelf CCD (Charge-coupled Device) camera and a novel target configuration and computation algorithm. In this method, a CCD camera, which can be mounted on either an adjacent vibrating structure or stationary support, is used to track global movements of a vibrating structure. The method utilizes subpixel feature extraction, Zhang’s camera calibration-based method, and a novel approach for target configuration which eliminates the need of having a stationary support for the recording camera. Two scaled models of a traffic signal structure were designed and constructed to implement the proposed method in laboratory conditions. The camera is mounted on one of the structures while facing targets on the other structure. The image data from the camera is processed to extract the structural response of both the structures in the field-of-view (FOV) of the camera as well as the structure on which the camera is mounted. Data from strain gages mounted on these structures provide measurement benchmarks for the dynamic structural responses. Experimental results indicate that one CCD camera and the presented method adequately capture displacements with similar accuracy and precision of strain gages. In practice, a camerabased method may be less expensive and more easily installed
Assessment of Pressed Panel Products using Camera Image Processing
The defect (necking, crack etc.) detection on press panels during the press forming process is an important step for ensuring the quality of the products. Traditional defect detection has been performed by human inspectors, which is subjective and expensive. It is also very difficult to apply traditional NDE/SHM approaches for such applications. Therefore, it is necessary to develop a new approach to efficiently detect such defects during the press forming process. In this study, we develop an automated crack detection technique for quality assessment of press panels with two camera image processing procedures. The first technique is evaluating the panel edge lines for crack detection, which are extracted from a percolated panel image object. This technique does not require a reference image for defect detection. The second technique is based on the comparison between a reference and a test image using the local image amplitude map. This process also contains compensation for misalignment of the images, which is typical in press lines. Defects are efficiently detected using two image processing techniques. For demonstration, experiments were performed in a laboratory and in actual manufacturing lines. Experimental results show that the proposed techniques could efficiently improve the defect detection rate with improved speed
Augmented Reality Tools for Enhanced Structural Inspection
To date, the tools available to structural inspectors to perform structural inspections have been very limited. In many cases inspectors are limited to using their eyes, ears, a tape measure and a hammer to perform an inspection. Notes are taken using a pad of paper and a pencil. In some cases, a tablet computer might be used to take notes along with a digital camera for documentation. Unfortunately, these techniques are clumsy and result in very few data points being measured and recorded. In this work, we will focus on developing the conceptual design for novel structural inspection tools for structural inspection applications based on emerging augmented reality technology. Augmented reality technology allows for holograms to be overlaid on the real world. It represents an exciting new way for inspectors to both collect, interact with, visualize, and analyze inspection data. The HoloLens allows a hands-free structural inspection and measurement technique minimizing time and challenges. The experiments conducted with the HoloLens for taking measurements and benchmarking the obtained measurements are shown in this study. It is proved that measurements taken from up to 20 feet distance, in and out of the line of sight, have less than 2% error compared to the actual measurement. Further applications of the HoloLens in the field of structural inspection are also discussed in this study.
Automated Region-of-interest Localization and Classification for Visual Assessment
Visual assessment is a process to understand the state of a structure based on evaluations originating from visual data. Low-cost, high-performance vision sensors are providing new avenues for overcoming the spatial and temporal limitation in current human-based visual assessment when used in conjunction with aerial sensing platforms. However, past implementations are limited in their ability to deal with a high volume of images while only a small fraction of them are important for actual inspection. Such difficulty induces an unwanted high rate of false-positive and negative errors, reducing the trustworthiness and efficiency of their implementation. To overcome this challenge, we develop and validate a novel automated image localization and classification technique to extract regions-of-interest (ROIs) on each of images, which contain the target region of the structure for visual evaluation (TRI). First, ROIs are extracted based on the geometric relationship between the collected images and the TRIs using structure-from-motion algorithm. Second, unwanted ROIs corrupted by occlusion and image blur are effectively filtered by a robust image classification technique, called convolutional neural network. Then, a damage detection technique is applied only on such highly relevant and localized ROI images. The capability of the technique is demonstrated using a full-scale highway sign structure for the case of crack detection on weld connections.
Camera-based Triggering of Bridge Structural Health Monitoring Systems using a Cyber-physical System Framework
A cyber-physical framework is proposed in this paper to integrate the measured response of two highway bridges and a weigh-in-motion system located along the same highway corridor. Leveraging traffic images captured by existing traffic cameras, computer vision techniques such as histogram of oriented gradients are utilized to detect trucks in images and synchronize measured vehicle-induced bridge data and truck weights with images of vehicle loads. Those images then act as a cyber-based linkage connecting the bridge monitoring systems and a weigh-in-motion system. Such a data fusion strategy makes it possible to establish a clear input-output model for each bridge system and explore the correlation between the responses of different bridges to same trucks. In addition, the camera system can also be used to trigger bridge monitoring systems intelligently to measure more truck events with a limited monitoring system power supply.
Combined Laser Scanning and Image Based Monitoring of Large Infrastructure Objects
This article focuses on new approaches where laser scanning and image data are used together to determine relevant deformations and deficiencies of large infrastructure objects. In the first case study, a large concrete arch dam in the Alps was measured with a scanning image assisted total station (IATS). We performed a laser scan of the dam and simultaneously recorded overview and telescopic images with the integrated cameras of the instrument. It is shown that the laser scanning data can be used together with the imaging data to automatically generate ortho images. These rectified images can be used to determine damages of the dam surface, to characterize these damages and to calculate their size. The second cases study focuses on retaining walls along highways. For this application we use a mobile measurement platform consisting of two laser scanners, an inertial measurement unit (IMU), a differential GNSS sensor and several cameras. We demonstrate that the combined analysis of the laser scanning and image data can be used to reliably determine and classify damages of retaining walls.
Computer Vision Based Inspection Approach to Predict Damage State and Load Level for RC Members
This paper focuses on a computer vision based inspection approach capable of relating surface damage observations to quantitative damage and load levels in structures. The approach is based on image processing and machine learning techniques which are used to build predictive models capable of estimating internal loads (i.e., shear and moment) and damage states in RC beams and slabs derived from surface crack pattern images. The presented predictive models have been developed and tested using image data sets obtained from 17 different earlier published studies, which provided about 900 crack pattern images captured from 130 RC beams and slabs with and without web reinforcement across a range of load and damage levels. These earlier studies focused on investigating different phenomena and parameters such as size effect, aggregate size, concrete strength, longitudinal and transverse steel ratio on shear strength of reinforced concrete beams. The collected data sets were divided into two major categories: (i) beams with web reinforcement; and (ii) beams without web reinforcement (i.e., beams intended to be shear-critical). Working with these existing image data sets, various textural and geometric attributes of surface crack patterns have been defined and evaluated with respect to their effectiveness in building useful regression predictive models. Rela-tively simple crack representations have been used, consistent with the varying nature of the images available in the earlier studies, but also with an eye toward potential field ap-plications in which image capture and segmentation quality could be limited. In general terms, the results support the potential utility of image-based estimations of load history and damage states of the components, with future applications in field inspection and post-disaster assessments
Computer Vision System for Real-Time Monitoring of Civil Infrastructures
Video-based vibration measurement is a cost-effective way for remote monitoring the health of conditions of transportation and other civil structures, especially for situations where accessibility is restricted and does not allow installation of conventional monitoring devices. Besides, video based system is global measurement. The technical basis of video-based remote vibration measurement system is digital image analyses. Comparison of the images allow the field of motion to be accurately delineated. Such information are important to understand the structural behaviors including the motion and strain distribution. This paper presents system and analyses to monitor the vibration velocity and displacement field. The performance is demonstrated on a testbed of model building. Three different methods (i.e., Frame Difference Method, Particle Image Velocimetry, and Optical Flow Method) are utilized to analyze the image sequences to extract the feature of motion. The performance is validated using accelerometer data. The results indicate that all three methods can estimate the velocity field of the model building, although the results can be affected by factors such as background noise and environmental interference. Optical flow method achieved the best performance among these three methods studied. With further refinement of system hardware and image processing software, it will be developed into a remote video based monitoring system for structural health monitoring of transportation infrastructure to assist the diagnosis of its health conditions
Corrosion Detection using Deep Convolutional Neural Networks
Regular inspection of civil infrastructure and mechanical systems is crucial for safe operations. Corrosion is an important type of deterioration in structural systems that can lead to catastrophic effects if untended. Manual inspection is currently the predominant method of inspection that is time-consuming, costly, tedious, and subjective. A less time consuming and inexpensive alternative to current corrosion monitoring methods is the use of optical instrumentation (e.g. digital cameras). Due to the recent advances in using Convolutional Neural Networks (CNNs), the vision-based classification performance of computers has been improved significantly. This study evaluates the use of a CNN for corrosion detection. The effect of different sliding window sizes used for classification is evaluated. The experimental results show that the performance of the CNN outperforms state-of-the-art vision-based corrosion detection algorithms
Evaluation of using Structure from Motion Optical Techniques for Structural Health Monitoring of Railroad Track
Routinely, civil, mechanical, and aerospace structures continue to be used despite that they are approaching or have already exceeded their design life. Structural monitoring techniques have the ability to indicate the onset of or avoid failure. Conventional sensing techniques like visual inspection and sensors such as accelerometers, strain gages, and acoustic transducers produce results at only a discrete number of points. Moreover, they can be labor intensive to install, have wire and power constraints, and the correlation between the sensor signal and structural integrity is not always straightforward. In the last few years, achievements made in camera technology, optical sensors, and image-processing algorithms allowed the development of a new generation of non-contact measuring methods. One of the most promising is Structure from Motion (SfM), a photogrammetric range imaging technique which makes it possible to obtain three-dimensional (3D) renderings from a cloud of two-dimensional images. The SfM approach has been widely used for large-area aerial inspections in the field of agriculture, geosciences, environmental disaster management, and heritage documentation. What is still missing is an evaluation of its feasibility for being used as a quantitative inspection technique for Structural Health Monitoring (SHM) purposes. In this study, an estimation of the accuracy of this non-contact, optically based measurement technique is experimentally performed. In particular, a number of laboratory experiments are executed on a real-size wood model of a railroad track to determine the capability of SfM in measuring crosstie vertical displacements, structural deformation, and geometry profiles. To validate the accuracy of the SfM measurements, a back-to-back comparison is made with Digital Image Correlation measurements. The strengths and limitations of the SfM approach for SHM are discussed and the measurement accuracy is quantified
Full-field Imaging and Modeling of Structural Dynamics with Digital Video Cameras
Structures usually have complex geometries, material properties, and boundary conditions, and exhibit spatially local, temporally transient, dynamic behaviors. High spatial and temporal resolution vibration measurements and modeling are thus required for high-fidelity characterization, analysis, and prediction of the structure’s dynamic phenomena. For example, high spatial density mode shapes are needed for accurate vibration-based damage localization. Also, higher order (frequency) vibration modes typically contain local structural features that are essential for high-fidelity modeling of the structure’s dynamics. In addition, while it is possible to build a highly-refined mathematical model (e.g., a finite element model) of the structure, it needs to be experimentally validated and updated with high-resolution vibration measurements. However, it is a significant challenge to obtain high-resolution vibration measurements using traditional techniques. For example, the widely-used accelerometers and straingauge sensors can only provide low spatial resolution measurements. Laser vibrometers provide high-resolution measurements, but are expensive and make sequential measurements that are time-consuming. On the other hand, photogrammetry as an alternative non-contact optical measurement method uses passive white-light imaging of digital video cameras that are relatively low-cost, agile, and provide non-contact, high spatial resolution, simultaneous, measurements where every pixel effectively becomes a measurement point on the structure. A new framework is developed for the blind extraction and realistic visualization of the full-field, high-resolution, dynamics behaviors of an operating structure from only its digital video measurements, possibly temporally-aliased (sub-Nyquist), using video motion manipulation and unsupervised machine learning techniques. This highresolution, full-field structural dynamics characterization framework addresses a variety of problems that traditionally have been challenging, including the ability to localize minute, non-visible, structural damage at a pixel resolution. Laboratory experiments on bench-scale building structures, cantilever beams, and stay cables are demonstrated.
Identification of Key Geometrical Features for Automatic Structural Damage Detection from Point Clouds
Digital technology to collect point cloud data has progressed dramatically in recent years along with its structural health monitoring applications. With these large data sets, the development of automated algorithms to detect structural damage from point clouds is critical to quickly and efficiently characterize structures and identify regions of interest. Lidar or laser scanners, popular equipment to collect point cloud data, can create highly detailed and geometry accurate three-dimensional point clouds from object surfaces, however the lack of discrete geometric information restrict their primary application to visualization or basic measurements (e.g., lineal, area, or volumetric computations). Various types of damage including cracking, concrete spalling, and loss of cross sectional areas in steel members due to corrosion can be detected by exploring local geometric variations of each vertex with respect to its neighboring vertices. To detect structural damage, an algorithm is developed to compute a series of geometrical features using discrete differential geometry theories and covariance geometrical features that can explicitly identify vertex variation. Application of such damage detection algorithm can have substantial effect on reducing field data collection time in comparison to traditional visual inspection along with more objective structural damage state characterization at a safe distance.
Integrating 3D Computer Vision and Robotic Infrastructure Inspection
Over the past decade, there has been growing interest in the field of robotic inspection systems, in particular Unmanned Aerial Vehicles (UAV). These systems afford many advantages in terms of reducing the cost of condition assessments and expediting the data acquisition while improving the monitoring process efficiency. However, robotic technologies are a means of collecting data, and data collection protocols must be tightly linked with the desired spatial data resolution and final products, particularly if integration with more conventional structural health monitoring technologies is to be attained. This process involves planning a set of views, physically altering the relative structureimaging sensor pose, globally registering all the acquired imagery data, and finally integrating different sources of data into a nonredundant model. In this context, this paper demonstrates the capabilities of using one such integration at the Brighton Dam, a complex and large-scale gravity dam in Maryland, USA. The goal of this study was to evaluate a robotic inspection methodology along with utilizing computer vision-based data analytics on photogrammetrically generated three-dimensional (3D) models of the dam. A series of experiments involving the analysis of both simulated and real structural damage were designed and performed. During these experiments, digital image data was collected from a variety of sources, including both fixed-wing and multi-rotor UAVs, as well as more conventional imaging platforms. These data sources were integrated and merged together to form a detailed high-resolution 3D model of the dam and surrounding environs, with varying level of details in damage and deterioration. 3D data processing techniques were then used to analyze these models to automatically detect and quantify various damages. The results of these tests reflect the capabilities of these powerful new monitoring techniques and indicate that a suite of vision-based algorithms is necessary for comprehensive analyses of these complex 3D models to reliably, and systematically detect damages. The findings of this study also reinforce the critical need for an in advance detailed planning along with team coordination during field data collection.
Level-of-detail Assessment of Structural Surface Damage using Spatially Sequential Stereo Images and Deep Learning Methods
In this paper, we report an innovative framework for automating structural surface damage assessment in engineering practice. Assessment of structural surface damage has been heavily relied on human-based inspection, which incurs significant cost to stakeholders of civil structures and infrastructure and often severe risk to the inspectors. Recognizing the promise of aerial robotics that can access dangerous locations and envisaging a future of structural inspection that ought to be fully autonomous, we have developed a framework, termed level-of-detail assessment of structural surface damage, that is geared towards real-time implementation for use in practice. The level-of-detail assessment is enabled by a remote sensing approach based on a small Unmanned Aerial Vehicle (UAV or drone) platform with an integrated payload of a low-cost stereo camera and a compact embedded computer. To achieve real-time detection, we propose the use of the faster region-based Convolution Neural Network (faster RCNN) as an artificial intelligence (AI) utility at different imaging depths. The stereo-camera based geometric reconstruction provides the basis of achieving level-of-detail quantitative damage assessment. In this paper, we further propose a novel data preparation method to accommodate the RCNN training. In the end, we will showcase some of these results based on our current implementation and experimental evaluation.
Long-term Structural Displacement Monitoring using Image Sequences and Spatio-Temporal Context Learning
In this study, a vision-based displacement measurement method using image sequences and spatio-temporal context (STC) learning is introduced for long-term structural displacement monitoring. Comparative study is carried out to verify the feasibility of the proposed method with current vision-based displacement measurement methods including (DIC, FLANN-SURF and LK-SURF) and the ground truth from LVDT under different adverse measuring conditions (including illumination changes and random occlusion induced by artificial mist). The results show that the proposed method has better robustness to illumination changes and random occlusion than current vision-based methods. The proposed method is promising in handling long-term structural displacement monitoring task in field.
Structural Health Monitoring from the Window Seat of a Passenger Airplane
Recent advances in computer vision and graphics have shown that regular, monocular cameras and video (e.g. cell phone cameras and Digital SLRs) can be used to identify the resonant frequencies of structures, and even image visually subtle operational deflection shapes. This paper is offered as a teaser for that work, focusing specifically on an example that may be of interest to people in the structural health monitoring (SHM) community. The discussion is high-level, and presented in an intentionally casual tone (much of the paper presents an anecdote – about recovering the operational deflection shape of an airplane wing using a cell phone and a dish sponge – using the first person). Our hope is to make this text as accessible and painless to read as possible, with hopes of introducing readers from different engineering disciplines to our related work in computer vision and graphics.
Real-Time Damage Detection of Engine Blade using Computer Vision and Machine Learning
In the process of engine maintenance, the most important thing is to find out where the damage is among a large number of blades. So far, the visual inspection method with industrial videoscope is mainly used for that. However, unfortunately, there are too many blades in engine to detect the damage precisely for an inspector. In this paper, we introduce a new method that can help the inspector to detect the damage without any omission and it is developed as a software for in-situ application. Our research has three steps: (1) pre-process step, (2) classification step and (3) tracking step. In the first step, we apply diverse image processing techniques to the digital images that are from the videoscope. As results of this step, scale invariant features are extracted and they are matched to the features from the damaged blade image according to the similarity. In the next step, it seeks damaged parts using the results of previous step and classifier which is already learned in order to classify the blade images into two groups, normal and damaged. The last step is optional because it operates when damage exists. After finding damage, it should track the damage as the videoscope camera moves. Now, our research is at the second step that only can recognizes the damaged parts in the videoscope images and the last step will be conducted at further research
Smaller than the Eye Can See: Selected Applications of Video-based Measurement
Cameras offer advantages over contact sensors, particularly in structures that might be difficult to instrument in a traditional manner. Recent work including motion magnification and phase-based optical flow methods enable the extraction of displacement, vibration, and structural information from videos with imperceptible subpixel motion. In this paper we present two case studies of interest to the structural health monitoring community. One case study involves the analysis of a video from the internal observation deck near the top of the Taipei 101 tower, showing the motion of the tuned mass damper during an earthquake. The other involves using high-speed video to measure the frequency responses of everyday objects as excited by a frequency sweep as reference measurements to identifying the object’s material by using separate measurements with a popular song as excitation
The Research and Application of Structural Health Monitoring Technology on the Coke Tower
The coke tower, work under high temperature, coke in and out with hot and cold distress through the cycle of 495℃ to 50℃, is the core reactor of the delayed coking device. Typical failure modes of bulge deformation of tower, low cycle fatigue crack of weld, material aging, the tower body tilting is easy to be caused under the comprehensive impact of complex load condition, temperature stress and mechanical stress of coke tower, which is a serious threat to safety production. For the sake of carrying out structure health monitoring technology and application research of the coke tower in a coking plant, acoustic emission, high temperature stress, temperature and tilt sensor technology was being used in this paper. And the system of the coke tower health monitoring and forecast management was also designed and developed. Based on the role of the system, real-time online monitoring data and off-line data can be acquired. The real-time on-line monitoring data can be used to diagnose the operating state of the structure. It is also possible to predict damage trends in critical areas through historical accumulation of off-line data. A new technical means based on structural health monitoring to guarantee the safe, healthy operation and guide the maintenance work of coke tower was put forward in the present paper.
Vision Based Displacement Sensor with Heat Haze Filtering Capability
The vision based sensors enable easy-to-install, non-contact, and low-cost structural displacement monitoring with high accuracy. The heat haze, however, will affect the accuracy of the vision based displacement sensors therefore affect the fidelity of the structural dynamic analysis. Heat haze effect was observed in the field tests and the image distortions due to heat haze were clearly seen in the videos. To eliminate the heat haze is vital in enabling vision displacement sensor to be used for long-term monitoring in hot weather, especially for long-distance multi-point monitoring. Few studies have been conducted on eliminating the effect of heat haze in vision based displacement sensor. This article presents a vision based displacement sensor incorporated with heat haze filtering capability. A heat haze filtering technique for the vision based displacement monitoring is presented based on a distortion rectification method and a new heat haze distortion base. The filtering technique is a statistical technique that first produces many sample images with simulated distortions with known warping parameters from an undistorted reference image using a distortion base. Then a distorted image obtained by the video camera is matched with the most similar sample image in terms of the shortest Euclidean distance. The warping parameters of the matched sample image is used to warp back the distorted image and to obtain a rectified image. The matching and warping process is repeated until the distance between the rectified image and the reference image falls below a threshold. The rectified image is used to extract heat haze filtered displacement measurement. In this paper, the effect of heat haze will first be detected and quantified. Then the heat haze filtering technique based on the new heat haze distortion base, will be introduced and implemented in the vision sensor. The performance of the heat haze filtering technique will be evaluated through laboratory tests and a field test with heat haze, by comparing the displacement results with and without the heat haze, with and without the heat haze filtering technique
Experimental Verification of Vision-based Cable Force Evaluation using a Smartphone
The portable and rapid cable force measurement for cable-supported structures, such as a cable-stayed bridge and a suspension bridge, has an important and practical significance in the evaluation of initial damage and the recovery of transportation networks after severe events such as earthquake and typhoon. Extracting natural frequencies of cable is considered as an essential issue in the cable force estimation. Therefore, in this study, a nontarget vision-based approach is proposed for identifying the natural frequencies of cable using handheld shooting of smartphone camera. The boundary of cable is selected as a target to be tracked in the region of interest (ROI) of video image sequence captured by smartphone camera, and the natural frequencies of cable are identified according to its dynamic displacement responses in frequency domain. The moving average is adopted to eliminate the noise associated with shaking of smartphone camera during measurement. A laboratory scale cable model test is carried out to evaluate the proposed approach. The results demonstrate the feasibility of vision-based cable force estimation using smartphone camera
Vision-based Crack Detection on Metallic Surfaces using Deep Convolutional Neural Network with Patch Clustering
In the U.S., there were eight nuclear power plant accidents cost more than 140 million US dollars in property damage. Regular inspection of the components of nuclear power plants is important to improve their resilience. However, current inspection practices are time consuming, tedious, and subjective: they involve operators manually locating cracks by watching videos. Very few of vision-based crack detection algorithms for nuclear power plant components has been developed. Prevalent crack detection algorithms for other applications cannot detect those cracks because they are typically very small and have low contrast. Moreover, the existence of scratches, welds, and grind marks on the surfaces leads to a large number of false positives when prevalent crack detection algorithms are used. The previous study applied local binary patterns (LBP) and support vector machine (SVM) to detect cracks in inspection videos and outperformed the other prevalent algorithms. This paper utilizes a deep convolutional neural network (CNN) to detect cracks in videos. The performance is evaluated using several inspection videos and it achieves more than 94% hit rate with only 0.5 false positive-per-frame which is much better than the previous study
DDDAS-based Joint Nonlinear Manifold Learning for Target Localization
The dynamic data-driven applications system (DDDAS) paradigm uses the controlled measurement data to update models. When big data streams are available, signal processing can be used for situation awareness. However, many times, the physical world prevents sensor measurements availability which provides an opportunity to use heterogeneous sensor information; but methods are needed for data normalization, sampling alignment, and data mining. In this paper, we highlight a joint nonlinear manifold learning approach to that incorporates advances in machine learning, data fusion, and model-based simulation propagators. The context of the situation monitoring includes a multiple moving targets, a video sensor, and distributed signal sensors. The challenge is to determine the moving emitter amongst a cluttered scene during an experiment with large data streams. Resolving the signature triangulation of the emitter with that of the video sensor is both a nonlinear tracking problem as well as data learning issue. The paper presents the joint nonlinear manifold learning approach that is theoretically developed such sensor models could not only be the tracking scenario; but that of structure health monitoring as well as internet of things (IoT) examples. The theoretical analysis, heterogeneous data set, machine learning, and results are presented to showcase the importance of real-time DDDAS modeling updates of kinematic models.
Structural Integrity Assessment of Corroded Landing Gear Drag
The reliability of a corroded helicopter landing gear drag beam has been investigated. The drag beam made of high strength steel has experienced a failure caused by corrosion initiated crack. The probability of fracture has been calculated from the intercept of a strength and a load distribution. The strength distribution of a drag beam has been obtained using thickness measurement data estimated by radiography. A model for thickness measurement has been developed using linear attenuation coefficient for both a base metal and a corrosion product. By comparing the photon intensities of corroded region with sound region of known thickness, the reduction of corrosion has been estimated. Due to uncertainties in model parameters which are the thickness of sound region, photon intensity with or without a corrosion product, and corrosion rate, Monte Carlo simulation has been conducted. A load distribution has been obtained using flight load data acquired from strain gages attached to the drag beams. The drag beam mostly carried compressive load and much smaller torsional load. The result showed that currently operated drag beams have enough margin of safety. With the consideration of uncertainties, the inspection of drag beam using radiography has sufficient capability for the reliability evaluation. Based on the risk of failure, part replacement schedule has been decided.
Comparison Analysis of German and Chinese Code for Deformation Measure
Deformation measurement plays an important role in the prevention of structural safety accidents. Every year, in order to make this process more convenient, lots of efforts have been made to develop new measurement technologies. However, deformation measurement is not just about the measurement technologies. The national code of practice for deformation measurement gives a standard protocol to accomplish deformation measurement properly according to certain measurement technology. DIN 18710:2010-09 is the German code of practice for engineering survey, which includes four parts. The fourth part (DIN 18710-4:2010-09) is the code for deformation measurement which is considered as an international level standard for deformation measurement. DIN 18710-4:2010-09 contains four chapters which are scope, normative references, terms and definitions and requirements. The forth chapter is divided into ten parts in the manner of life cycle of the project of deformation measurement. In China, JGJ 8-2016 is the latest code for deformation measurement of building and structure. All the deformation measurement works in China are conducted based on this code. Both these two codes are introduced in this paper. In the conclusion part, a brief comparison is given
Development of a Novel Osseointegrated Endoprosthesis, Combing Orthopaedic and Engineering Design Principles, and Structural Health Monitoring Conc
The structural health and long-term survival of orthopedic endoprosthetic constructs are currently limited by lack of direct monitoring capabilities and by implant design. In our previous studies, we have successfully applied Structural Health Monitoring (SHM) concepts to develop methods to monitor the progress of bone healing1,2 and the extent of osseointegration of endoprostheses, in order to predict and prevent construct failure. The objective of this project is to develop a novel osseointegration implant for transfemoral amputees. Currently available implants result in stress shielding and progressive loss of bone substance, both of which limit construct longevity and, in the event of construct failure, limit options for revision surgery. In this project, we have combined orthopedic and engineering design principles to develop a shape-optimized and stress-optimized endo-exo prosthesis, that mitigates stress shielding and progressive loss of bone substance, and incorporates sensing elements to facilitate through-life Structural Health Monitoring. The implant is designed to optimize implant longevity, employ direct structural health monitoring to prevent catastrophic construct failure, and maximize the options for revision in the event of predicted or realized failure. In this paper, we discuss the clinical and design considerations of the proposed implant, and the role of SHM in the design and monitoring processes
Application of Interface Waves for Damage Detection in Laminated Composite B
This research investigates a novel ultrasonic method using guided interface waves for the inspection of curved hybrid laminate composite structures. Most composite airframe components, such as skin – stringer and web - flange attachment of beams and channels, form structural elements which are known as Noodles. Noodles or radius fillers are used to fill-out the radius bend of curved structures. Noodles are constructed from composites with orientations different from the primary laminated structure. Noodles are vulnerable to many forms of damages and flaws. Noodles are also located in areas that might not be very convenient for inspection by typical nondestructive methods. In this research, the propagation of ultrasonic guided interface waves in a skin – stringer configuration with noddle is studied. The surface acoustic wave on the curved outer surface is modeled. The intention is to identify effective guided waves for the structural curvature radius inspection. The guided wave propagation path along the bondline of skin – stringer and noodle area is simulated numerically by finite element model. Typical damage scenarios in the noodle area are modeled. The ultrasonic interface wave behavior is studied for several cases to characterize noodle damages. The attenuation and scattering of wave-front suggest the interface wave can detect several damages at certain frequency range, when the wavelength is compatible with the bend radius of the structure. Also the location of damage in the noodle area can be found by reflection sources from different defect sizes. This study can be used for a generalized solution of noodle inspection problem and in connection with examination of curved surface as part of the development of a structural health monitoring system
Structural Hybrid Layer for Critical Strain Detection in Composite Parts: A Multifunctional Appr