Global failure probability function estimation based on an adaptive strategy and combination algorithm

Yuan, Xiukai, Qian, Yugeng, Chen, Jingqiang, Faes, Matthias GR, Valdebenito, Marcos A and Beer, Michael ORCID: 0000-0002-0611-0345 (2023) Global failure probability function estimation based on an adaptive strategy and combination algorithm. RELIABILITY ENGINEERING & SYSTEM SAFETY, 231. p. 108937.

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Abstract

The failure probability function (FPF) expresses the probability of failure as a function of the distribution parameters associated with the random variables of a reliability problem. Knowledge on this FPF is of much relevance for reliability sensitivity analysis and reliability-based design optimisation. However, its calculation is usually a challenging task. Therefore, this paper presents an efficient approach for estimating the FPF based on an adaptive strategy and a combination algorithm. The proposed approach involves three basic elements: (1) a Weighted Importance Sampling approach, which allows determining local FPF estimates; (2) an adaptive strategy for determining at which realisations of the distribution parameters it is necessary to perform local FPF estimation; and (3) an optimal combination algorithm, which allows to aggregate local FPF estimations together to form a global estimate of the FPF. Test and practical examples are presented to demonstrate the efficiency and feasibility of the proposed approach.

Item Type:	Article
Uncontrolled Keywords:	Failure probability function, Importance sampling, Combination algorithm, Adaptive strategy
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	13 Jun 2023 15:51
Last Modified:	12 Nov 2023 02:30
DOI:	10.1016/j.ress.2022.108937
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3170907