Bayesian method for states estimation in structural health monitoring application

摘要

An important goal in structural health monitoring (SHM) is to identify the state of the structure in order to detect when damage occurs. In this paper, we tackle the state estimation problem in SHM systems. Therefore, a Bayesian approach based on particle filtering is proposed to perform the optimal online estimation. The proposed scheme relies on the introduction of an efficient importance density, based on the iterated square root central difference Kalman filter (ISRCDKF), which takes into consideration the current observation. The performance of this method is studied considering a complex three degree of freedom spring-mass-dashpot system. Simulation results show the efficiency of the suggested approach in terms of Root Mean Square Error (RMSE).