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Multilevel Monte Carlo for Reliability Theory

Aslett, L.J.M.; Nagapetyan, T.; Vollmer, S.J.

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Authors

T. Nagapetyan

S.J. Vollmer



Abstract

As the size of engineered systems grows, problems in reliability theory can become computationally challenging, often due to the combinatorial growth in the number of cut sets. In this paper we demonstrate how Multilevel Monte Carlo (MLMC) — a simulation approach which is typically used for stochastic differential equation models — can be applied in reliability problems by carefully controlling the bias-variance tradeoff in approximating large system behaviour. In this first exposition of MLMC methods in reliability problems we address the canonical problem of estimating the expectation of a functional of system lifetime for non-repairable and repairable components, demonstrating the computational advantages compared to classical Monte Carlo methods. The difference in computational complexity can be orders of magnitude for very large or complicated system structures, or where the desired precision is lower.

Citation

Aslett, L., Nagapetyan, T., & Vollmer, S. (2017). Multilevel Monte Carlo for Reliability Theory. Reliability Engineering & System Safety, 165, 188-196. https://doi.org/10.1016/j.ress.2017.03.003

Journal Article Type Article
Acceptance Date Mar 6, 2017
Online Publication Date Mar 9, 2017
Publication Date Sep 1, 2017
Deposit Date Apr 24, 2017
Publicly Available Date Apr 26, 2017
Journal Reliability Engineering and System Safety
Print ISSN 0951-8320
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 165
Pages 188-196
DOI https://doi.org/10.1016/j.ress.2017.03.003

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