An adaptive subset simulation algorithm for system reliability analysis with discontinuous limit states

Many system reliability problems involve performance functions with a discontinuous distribution. Such situations occur in both connectivity- and flow-based network reliability problems, due to binary or multi-state random variables entering the definition of the system performance or due to the dis...

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Vydáno v:Reliability engineering & system safety Ročník 225; s. 108607
Hlavní autoři: Chan, Jianpeng, Papaioannou, Iason, Straub, Daniel
Médium: Journal Article
Jazyk:angličtina
Vydáno: Barking Elsevier Ltd 01.09.2022
Elsevier BV
Témata:
Adaptive algorithms
Adaptive sampling
Algorithms
Conditional probability
Conditional sampling
Discontinuity
Domains
Independent Metropolis–Hastings
Limit state function with discontinuous distribution
Limit states
Network reliability
Random variables
Reliability analysis
Reliability engineering
Sampling methods
Simulation
Subset simulation
System reliability
System reliability analysis
Subset simulation
Limit state function with discontinuous distribution
Conditional sampling
System reliability analysis
Independent Metropolis–Hastings
ISSN:0951-8320, 1879-0836
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Shrnutí:Many system reliability problems involve performance functions with a discontinuous distribution. Such situations occur in both connectivity- and flow-based network reliability problems, due to binary or multi-state random variables entering the definition of the system performance or due to the discontinuous nature of the system model. When solving this kind of problems, the standard subset simulation algorithm with fixed intermediate conditional probability and fixed number of samples per level can lead to substantial errors, since the discontinuity of the output can result in an ambiguous definition of the sought percentile of the samples and, hence, of the intermediate domains. In this paper, we propose an adaptive subset simulation algorithm to determine the reliability of systems whose performance function is a discontinuous random variable. The proposed algorithm chooses the number of samples and the intermediate conditional probabilities adaptively. We discuss two MCMC algorithms for generation of the samples in the intermediate domains, the adaptive conditional sampling method and a novel independent Metropolis–Hastings algorithm that efficiently samples in discrete input spaces. The accuracy and efficiency of the proposed algorithm are demonstrated by a set of numerical examples. •We introduce adaptive effort subset simulation for network reliability assessment.•The proposed algorithm modifies the adaptive selection of the intermediate domains.•We propose a novel independent M–H sampler to sample in discrete input spaces.•Numerical results demonstrate the efficiency and accuracy of the proposed method.
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ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2022.108607