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Application of dynamic evidential networks in reliability analysis of complex systems with epistemic uncertainty and multiple life distributions

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  • Jinhua Mi

    (School of Automation Engineering, University of Electronic Science and Technology of China
    Center for System Reliability and Safety, University of Electronic Science and Technology of China
    Institute for Risk and Reliability, Leibniz University Hannover)

  • Yuhua Cheng

    (School of Automation Engineering, University of Electronic Science and Technology of China)

  • Yufei Song

    (School of Automation Engineering, University of Electronic Science and Technology of China)

  • Libing Bai

    (School of Automation Engineering, University of Electronic Science and Technology of China)

  • Kai Chen

    (School of Automation Engineering, University of Electronic Science and Technology of China)

Abstract

With the modernization and intelligent of industrial equipment and systems, the challenges of dynamic characteristics, failure dependency and uncertainties have aroused by the increasing of system complexity. Besides, various types of components may follow different life distributions which bring the multiple life distributions problem in systems. In order to model the impact of time dependency and epistemic uncertainty on the failure behavior of system, this paper combines the flexible dynamic modeling with the uncertainty expression. Its advantages are intuitively graphical representation and reasoning that brought by evidential network (EN). After that, the discrete time dynamic evidential network (DT-DEN) is introduced to analyze the reliability of complex systems, and the network inference mechanism is clearly defined. The evidence theory and original definition and inference mechanism of conventional EN is firstly recommended, and the DT-DEN is further presented. Furthermore, the multiple life distributions are synthesized into the DT-DEN to tackle the epistemic uncertainty and mixed life distribution challenges. Specifically, the dynamic logic gates are converted into equivalent DENs with distinguished conditional mass tables, and then the belief interval of system reliability can be calculated by network forward reasoning. Finally, the availability and efficiency of the proposed method is verified by some numerical examples.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:311:y:2022:i:1:d:10.1007_s10479-019-03211-4
    DOI: 10.1007/s10479-019-03211-4
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    References listed on IDEAS

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