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Failure probability estimation of a class of series systems by multidomain Line Sampling

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  • Valdebenito, Marcos A.
  • Wei, Pengfei
  • Song, Jingwen
  • Beer, Michael
  • Broggi, Matteo

Abstract

This contribution proposes an approach for the assessment of the failure probability associated with a particular class of series systems. The type of systems considered involves components whose response is linear with respect to a number of Gaussian random variables. Component failure occurs whenever this response exceeds prescribed deterministic thresholds. We propose multidomain Line Sampling as an extension of the classical Line Sampling to work with a large number of components at once. By taking advantage of the linearity of the performance functions involved, multidomain Line Sampling explores the interactions that occur between failure domains associated with individual components in order to produce an estimate of the failure probability. The performance and effectiveness of multidomain Line Sampling is illustrated by means of two test problems and an application example, indicating that this technique is amenable for treating problems comprising both a large number of random variables and a large number of components.

Suggested Citation

  • Valdebenito, Marcos A. & Wei, Pengfei & Song, Jingwen & Beer, Michael & Broggi, Matteo, 2021. "Failure probability estimation of a class of series systems by multidomain Line Sampling," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:reensy:v:213:y:2021:i:c:s0951832021002118
    DOI: 10.1016/j.ress.2021.107673
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Zhang, Long-Wen & Dang, Chao & Zhao, Yan-Gang, 2023. "An efficient method for accessing structural reliability indexes via power transformation family," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    2. Dang, Chao & Valdebenito, Marcos A. & Wei, Pengfei & Song, Jingwen & Beer, Michael, 2024. "Bayesian active learning line sampling with log-normal process for rare-event probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    3. Liu, Gang & Gao, Kai & Yang, Qingshan & Tang, Wei & Law, S.S., 2021. "Improvement to the discretized initial condition of the generalized density evolution equation," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    4. Yuan, Xiukai & Zheng, Weiming & Zhao, Chaofan & Valdebenito, Marcos A. & Faes, Matthias G.R. & Dong, Yiwei, 2024. "Line sampling for time-variant failure probability estimation using an adaptive combination approach," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    5. Hong, Fangqi & Wei, Pengfei & Fu, Jiangfeng & Beer, Michael, 2024. "A sequential sampling-based Bayesian numerical method for reliability-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    6. Song, Jingwen & Cui, Yifan & Wei, Pengfei & Valdebenito, Marcos A. & Zhang, Weihong, 2024. "Constrained Bayesian optimization algorithms for estimating design points in structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    7. Ajenjo, Antoine & Ardillon, Emmanuel & Chabridon, Vincent & Cogan, Scott & Sadoulet-Reboul, Emeline, 2023. "Robustness evaluation of the reliability of penstocks combining line sampling and neural networks," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    8. Wang, Yanzhong & Xie, Bin & E, Shiyuan, 2022. "Adaptive relevance vector machine combined with Markov-chain-based importance sampling for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).

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