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An Efficient Method for Failure Probability-based Moment-independent Importance Measure
Received date: 2013-11-12
Revised date: 2013-12-02
Online published: 2013-12-17
Supported by
National Natural Science Foundation of China (51175425); Research Fund for the Doctoral Program of Higher Education of China (20116102110003)
The failure probability-based moment-independent importance measure can well analyze the effect of input uncertainties on the failure probability of a structure or system. However, compared with the variance-based importance measure, there are few accurate and efficient methods for the computation of the moment-independent importance measure at present. In this context, a highly efficient method to compute the failure probability-based moment-independent importance measure is proposed. The proposed method estimates efficiently the conditional probability density function of the model output using the fractional moments and high-dimensional model representation-based maximum entropy method, thus the conditional failure probability can be easily obtained by integration. Finally the three-point estimation method is applied to computing the variance, namely the failure probability-based moment-independent importance measure. Since the advantages of the maximum entropy method and the three-point estimation method are inherited directly, the proposed method can yield accurate results under a small number of function evaluations. Examples in the paper demonstrate the advantages of the proposed method as compared with existing methods, and indicate its good prospect for engineering application.
ZHANG Leigang , LYU Zhenzhou , CHEN Jun . An Efficient Method for Failure Probability-based Moment-independent Importance Measure[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2014 , 35(8) : 2199 -2206 . DOI: 10.7527/S1000-6893.2013.0483
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