Conditional probability Markov chain simulation based reliability analysis method for nonnormal variables

作     者：YUAN XiuKai,LU ZhenZhou & QIAO HongWei School of Aeronautics,Northwestern Polytechnical University,Xi’an 710072,China 

作者机构：School of Aeronautics Northwestern Polytechnical University Xi’an China 

出 版 物：《Science China(Technological Sciences)》 (中国科学（技术科学英文版）)

年 卷 期：2010年第53卷第5期

页      面：1434-1441页

核心收录：

学科分类：080202[工学-机械电子工程] 08[工学] 0802[工学-机械工程] 

基　　金：supported by the National Natural Science Foundation of China (Grant No.50875213) the Aviation Science Foundation (Grant No.2007ZA53012) the National Hi-Tech Research and Development Program of China ("863" Project)(Grant No.2007AA04Z401) 

主　　题：reliability failure probability Markov chain Monte Carlo 

摘      要：Based on fast Markov chain simulation for generating the samples distributed in failure region and saddlepoint approximation(SA) technique,an efficient reliability analysis method is presented to evaluate the small failure probability of non-linear limit state function(LSF) with non-normal *** the presented method,the failure probability of the non-linear LSF is transformed into a product of the failure probability of the introduced linear LSF and a feature ratio *** introduced linear LSF which approximately has the same maximum likelihood points as the non-linear LSF is constructed and its failure probability can be calculated by SA *** feature ratio factor,which can be evaluated on the basis of multiplicative rule of probability,exhibits the relation between the failure probability of the non-linear LSF and that of the linear LSF,and it can be fast computed by utilizing the Markov chain algorithm to directly simulate the samples distributed in the failure regions of the non-linear LSF and those of the linear ***,the expectation and variance of the failure probability estimate are *** results of several examples demonstrate that the presented method has wide applicability,can be easily implemented,and possesses high precision and high efficiency.