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

authored by: Xiukai Yuan, Yugeng Qian, Jingqiang Chen, Matthias G. R. Faes, Marcos A. Valdebenito, Michael Beer
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.
Organisation(s): Institute for Risk and Reliability
External Organisation(s): Xiamen University
TU Dortmund University
University of Liverpool
Tongji University
Type: Article
Journal: Reliability Engineering and System Safety
Volume: 231
No. of pages: 1
ISSN: 0951-8320
Publication date: 03.2023
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Safety, Risk, Reliability and Quality, Industrial and Manufacturing Engineering
Electronic version(s): https://doi.org/10.1016/j.ress.2022.108937 (Access: Closed)