Details
| Original language | English |
|---|---|
| Article number | 108937 |
| Number of pages | 1 |
| Journal | Reliability Engineering and System Safety |
| Volume | 231 |
| Early online date | 12 Nov 2022 |
| Publication status | Published - Mar 2023 |
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
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Reliability Engineering and System Safety, Vol. 231, 108937, 03.2023.
Research output: Contribution to journal › Article › Research › peer review
}
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 - iukai Yuan would like to acknowledge financial support from NSAF, China (Grant No. U1530122), the Aeronautical Science Foundation of China (Grant No. ASFC-20170968002).
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
UR - http://www.scopus.com/inward/record.url?scp=85149360454&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2022.108937
DO - 10.1016/j.ress.2022.108937
M3 - Article
VL - 231
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
SN - 0951-8320
M1 - 108937
ER -