Structure reliability and response prognostics under uncertainty using bayesian analysis and analytical approximations

This study presents an efficient method for system reliability and response prognostics based on Bayesian analysis and analytical approximations. Uncertainties are explicitly included using probabilistic modeling. Usage and health monitoring information is used to perform the Bayesian updating. To improve the computational efficiency, an analytical computation procedure is proposed and formulated to avoid time-consuming simulations in classical methods. Two realistic problems are presented for demonstrations. One is a composite beam reliability analysis, and the other is the structural frame dynamic property estimation with sensor measurement data. The overall efficiency and accuracy of the proposed method is compared with the traditional simulation-based method.