Structural Performance Monitoring Employing Linear Observer

Abstract

Structural performances are heavily relying on the overall health conditions (damaged or healthy) of any dynamical system. Hence, it is essential to keep them monitored to avoid any partial/fully damaged situation. Due to the availability of the modern technologies, the monitoring tasks are done by employing sensors to reduce manual effort. However, the structural health monitoring via sensors deployment comes with a cost even considering all the merits of modern monitoring approach. Therefore, it is realistic to have a reasonable number of sensors on the structures in order to avoid financial hurdle or to make things more feasible. In order to minimize the sensors number, in this study, the investigations have been done via employing finite number of sensors. As a result, it might be tricky to obtain the missing states of the degree-of-freedom where sensor was not placed. Herein, the missing states are estimated by adopting a linear type observer e.g. Kalman filter as all of the states have not been observed. The numerical simulations have been performed by considering a 7-storey structure in a nearly real-time scenario via the use of MATLAB and SIMULINK. To achieve the optimal performance, the Kalman Gain was also estimated real-time by solving the Riccati equation of the investigated system. The performance of the investigated problem has been evaluated under healthy and different equivalent damaged conditions by adding external noise quantities to the healthy signals. In a nutshell, it is observed that the observer is capable of rendering the original behavior of the structure quite accurately under both healthy and damaged conditions. However, it has been observed that with the significant level of noise the observer struggles a lot attain optimal performances.