An Adaptive Extended Kalman Filter For Structural Damage Identification

An important objective of structural health monitoring systems is to identify the state of the structure and to detect the damage when it occurs. Analysis techniques for damage identification of structures, based on vibration data measured from sensors, have received considerable attention. Recently, a new adaptive tracking technique, based on the extended Kalman filter approach, has been proposed for the damage identification of structures. The proposed technique is capable of tracking the changes of system parameters from which the event and severity of structural damage may be detected online. Simulation results for tracking the parametric changes of nonlinear elastic, hysteretic and linear structures are presented to demonstrate the application and effectiveness of the proposed technique in detecting structural damage. In this paper, we also present experimental studies to verify the capability of the AEKF approach in identifying the structural damage by conducting a series of experimental tests. To simulate structural damages during the test, an innovative device is proposed to reduce the stiffness of some stories. Different damage scenarios have been simulated and tested. Measured response data and the AEKF approach are used to track the variation of stiffness during the test. The tracking results for stiffness are then compared with the stiffness predicted by the finite-element method. Experimental results demonstrate that the AEKF approach is capable of tracking the variation of structural parameters leading to the detection of structural damages.