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论文类型： 期刊论文

第一作者： Li, Gang

通讯作者： Zeng, Y (reprint author), Dalian Univ Technol, Dept Engn Mech, State Key Lab Struct Anal Ind Equipment, Dalian 116024, Peoples R China.

合写作者： Zhou, Chunxiao,Zeng, Yan,He, Wanxin,Li, Haoran,Wang, Ruiqiong

发表时间： 2019-02-01

发表刊物： APPLIED MATHEMATICAL MODELLING

收录刊物： SCIE、EI

卷号： 66

页面范围： 26-40

ISSN号： 0307-904X

关键字： Reliability analysis; Univariate dimension reduction method; Maximum entropy method; Nonlinear transformation

摘要： A new algorithm based on nonlinear transformation is proposed to improve the classical maximum entropy method and solve practical problems of reliability analysis. There are three steps in the new algorithm. Firstly, the performance function of reliability analysis is normalized, dividing by its value when each input is the mean value of the corresponding random variable. Then the nonlinear transformation of such normalized performance function is completed by using a monotonic nonlinear function with an adjustable parameter. Finally, the predictions of probability density function and/or the failure probability in reliability analysis are achieved by looking the result of transformation as a new form of performance function in the classical procedure of maximum entropy method in which the statistic moments are given through the univariate dimension reduction method. In the proposed method, the uncontrollable error of integration on the infinite interval is removed by transforming it into a bounded one. Three typical nonlinear transformation functions are studied and compared in the numerical examples. Comparing with results from Monte Carlo simulation, it is found that a proper choice of the adjustable parameter can lead to a better result of the prediction of failure probability. It is confirmed in the examples that result from the proposed method with the arctangent transformation function is better than the other transformation functions. The error of prediction of failure probability is controllable if the adjustable parameter is chosen in a given interval, but the suggested value of the adjustable parameter can only be given empirically. (C) 2018 Elsevier Inc. All rights reserved.