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Hybrid computation of uncertainty in reliability analysis with p-box and evidential networks

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  • Simon, Christophe
  • Bicking, Frédérique

Abstract

This paper presents a method to assess system reliability in the presence of epistemic and aleatory uncertainty. This hybrid method uses belief functions to model and manipulate uncertainty. P-boxes are used to represent basic uncertainties and acyclic directed networks to model the system reliability. These choices allow a flexible modeling of uncertainty, while limiting the computational cost of inferences. In particular, they offer convenient ways of integrating expert opinions and many kinds of uncertainty sources. It also can model complex systems. In this paper, we introduce the modeling method and apply it to a fire detection system with different kinds of input p-boxes for the sake of illustration.

Suggested Citation

  • Simon, Christophe & Bicking, Frédérique, 2017. "Hybrid computation of uncertainty in reliability analysis with p-box and evidential networks," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 629-638.
    • Handle: RePEc:eee:reensy:v:167:y:2017:i:c:p:629-638
    DOI: 10.1016/j.ress.2017.04.015
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    References listed on IDEAS

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    Cited by:

    1. Liu, Yang & Wang, Dewei & Sun, Xiaodong & Liu, Yang & Dinh, Nam & Hu, Rui, 2021. "Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    2. Qiu, Siqi & Ming, Xinguo, 2020. "An extended Birnbaum importance-based two-stage heuristic for component assignment problems under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    3. Yuan, Xiukai & Faes, Matthias G.R. & Liu, Shaolong & Valdebenito, Marcos A. & Beer, Michael, 2021. "Efficient imprecise reliability analysis using the Augmented Space Integral," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    4. Jinhua Mi & Yuhua Cheng & Yufei Song & Libing Bai & Kai Chen, 2022. "Application of dynamic evidential networks in reliability analysis of complex systems with epistemic uncertainty and multiple life distributions," Annals of Operations Research, Springer, vol. 311(1), pages 311-333, April.
    5. Salomon, Julian & Winnewisser, Niklas & Wei, Pengfei & Broggi, Matteo & Beer, Michael, 2021. "Efficient reliability analysis of complex systems in consideration of imprecision," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. Song, Yufei & Mi, Jinhua & Cheng, Yuhua & Bai, Libing & Chen, Kai, 2020. "A dependency bounds analysis method for reliability assessment of complex system with hybrid uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    7. Rocchetta, Roberto & Patelli, Edoardo, 2020. "A post-contingency power flow emulator for generalized probabilistic risks assessment of power grids," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    8. Mi, Jinhua & Lu, Ning & Li, Yan-Feng & Huang, Hong-Zhong & Bai, Libing, 2022. "An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 220(C).

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