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

Xiukai Yuan; Yugeng Qian; Jingqiang Chen; Matthias G.R. Faes; Marcos A. Valdebenito; Michael Beer

doi:10.1016/j.ress.2022.108937

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

Xiukai Yuan, Yugeng Qian, Jingqiang Chen, Matthias G.R. Faes, Marcos A. Valdebenito, Michael Beer

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

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.

Original language	English
Article number	108937
Journal	Reliability Engineering and System Safety
Volume	231
DOIs	https://doi.org/10.1016/j.ress.2022.108937
State	Published - Mar 2023
Externally published	Yes

Keywords

Adaptive strategy
Combination algorithm
Failure probability function
Importance sampling

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10.1016/j.ress.2022.108937

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@article{dcd5415ba8214a8d91fcc5e19ea5236d,

title = "Global failure probability function estimation based on an adaptive strategy and combination algorithm",

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.",

keywords = "Adaptive strategy, Combination algorithm, Failure probability function, Importance sampling",

author = "Xiukai Yuan and Yugeng Qian and Jingqiang Chen and Faes, {Matthias G.R.} and Valdebenito, {Marcos A.} and Michael Beer",

note = "Funding Information: Xiukai Yuan would like to acknowledge financial support from NSAF, China (Grant No. U1530122 ), the Aeronautical Science Foundation of China (Grant No. ASFC-20170968002 ). Publisher Copyright: {\textcopyright} 2022 Elsevier Ltd",

year = "2023",

month = mar,

doi = "10.1016/j.ress.2022.108937",

language = "English",

volume = "231",

journal = "Reliability Engineering and System Safety",

issn = "0951-8320",

publisher = "Elsevier Ltd.",

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TY - JOUR

T1 - Global failure probability function estimation based on an adaptive strategy and combination algorithm

AU - Yuan, Xiukai

AU - Qian, Yugeng

AU - Chen, Jingqiang

AU - Faes, Matthias G.R.

AU - Valdebenito, Marcos A.

AU - Beer, Michael

N1 - Funding Information: Xiukai Yuan would like to acknowledge financial support from NSAF, China (Grant No. U1530122 ), the Aeronautical Science Foundation of China (Grant No. ASFC-20170968002 ). Publisher Copyright: © 2022 Elsevier Ltd

PY - 2023/3

Y1 - 2023/3

N2 - 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.

AB - 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.

KW - Adaptive strategy

KW - Combination algorithm

KW - Failure probability function

KW - Importance sampling

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DO - 10.1016/j.ress.2022.108937

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SN - 0951-8320

VL - 231

JO - Reliability Engineering and System Safety

JF - Reliability Engineering and System Safety

M1 - 108937

ER -

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

Abstract

Keywords

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