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 -