Influence of input PDF parameters of a model on a failure probability estimation

被引:28
|
作者
Morio, Jerome [1 ]
机构
[1] Off Natl Etud & Rech Aerosp, FR-91123 Palaiseau, France
关键词
Sensitivity analysis; Rare event simulation; Monte Carlo methods; Sobol indices; Importance sampling; Importance splitting; GLOBAL SENSITIVITY-ANALYSIS; OPTIMIZATION; UNCERTAINTY; SIMULATION;
D O I
10.1016/j.simpat.2011.08.003
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Critical probability estimation is of major interest in safety and reliability applications. In this article, we focus on a black-box model with multidimensional random input X and one random output Y. We consider the estimation of probability P that Y exceeds a threshold S. We assume that the random input X follows a multidimensional parametric density with parameters delta and thus the probability P will depend on the values of delta. In this paper, we analyze the sensitivity of the critical probability P to the model parameters delta. We propose a methodology that estimates Sobol indices with low computation cost. This strategy enables us to determine which statistical parameters have a great influence on the value of the probability and require a valuable determination. The last part of this article applies the proposed technique on a realistic case of missile collateral damage estimation. (C) 2011 Elsevier B.V. All rights reserved.
引用
收藏
页码:2244 / 2255
页数:12
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