In the time-variant systems, random variables, stochastic processes, and time parameter are regarded as the inputs of time-variant computational model. This results in an even more computationally expensive model what makes the time-variant reliability analysis a challenging task. This paper addresses the problem by presenting an active learning strategy using polynomial chaos expansion (PCE) in an augmented reliability space. We first propose a new algorithm that determines the sparse representation applying statistical threshold to determine the significant terms of the PCE model. This adaptive decision test is integrated into the variational Bayesian method, improving its accuracy and reducing convergence time. The proposed method uses a composite criterion to identify the most significant time instants and the associated training points to enrich the experimental design. By simulations, we compare the performance of the proposed method with respect to other existing time-variant reliability analysis methods.
An Improved High-Moment Method for Reliability Analysis Based on Polynomial Chaos Expansion
