Hybrid SHM Sensor Network Feeding Multiple SHM Systems for Monitoring Plate-Like Structures Under Cyclic Fatigue: First Results of a Dataset Under Construction

Abstract

Several Structural Health Monitoring (SHM) technologies and approaches have been developed so far to continuously monitor the condition of a structure. They can be cate- gorized as active and passive systems, giving different insights regarding the monitored specimen when it comes to damage detection. Regardless of the specific system, the goals are to detect, localize, and characterize emerging defects much prior to they can induce catastrophic failure. However, qualifying such a condition monitoring system is paramount to achieve industrial deployment. Nonetheless, assessing reliability of the resulting system remains a challenging task, even when resorting to standard methods such as probability of detection. In general, it is hard to simulate all the influencing factors through laboratory tests and demonstrate whether the qualification procedure can be transferred from specimen to specimen or whether it is strongly system dependent. Having these challenges in mind, this paper presents the first few specimens of a novel fatigue crack dataset under construction recorded from a hybrid sensor network feeding multiple SHM systems based on ultrasonic guided waves, electro-mechanical impedance, acoustic emission, fiber Bragg gratings and any combination thereof. The idea of this data set is to publish it as open access in order to enable people and especially early-career researchers and students to test and explore including aspects like specimen- to-specimen variability, data merging, etc.