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

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外文摘要：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 variables. In 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 factor. The 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 technique. The 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 LSF. Moreover, the expectation and variance of the failure probability estimate are derived. The results of several examples demonstrate that the presented method has wide applicability, can be easily implemented,and possesses high precision and high efficiency.

外文关键词：

reliabilityfailure probabilityMarkov chainMonte Carlo

作者：

YUAN XiuKai、LU ZhenZhou、QIAO HongWei

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作者单位：

School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China

基金：

This work was supported by the National Natural Science Foundation of ChinaAviation Science FoundationNational Hi-Tech Research and Development Program of China ("863" Project)

项目编号：

508752132007ZA530122007AA0401

出版年：

2010

DOI：

10.1007/s11431-010-0138-9

中国科学:技术科学(英文版)

中国科学院

中国科学:技术科学(英文版)

SCIEI

影响因子：1.056

ISSN：1674-7321

年,卷(期)：2010.53(5)

被引量5
参考文献量17