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A general framework of Bayesian network for system reliability analysis using junction tree

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  • Byun, Ji-Eun
  • Song, Junho

Abstract

To perform the reliability analysis of complex and large-scale systems, Bayesian network (BN) can be useful as it facilitates modelling the causal relationship between multiple types of variables, e.g. hazards, material properties, and inspection results. However, its conventional approach shows limitations in handling large-scale systems and advanced inference tasks such as continuous distributions and approximate inference. On the other hand, these issues have been successfully addressed by system reliability analysis (SRA) theory, while the complexity of system reliability methods (SRMs) makes it challenging to handle multiple types of variables collectively. Accordingly, to facilitate the reliability analysis of real-world problems, this paper develops a general framework to implement BN for SRA by employing junction tree (JT). The connection between BN and SRA is further consolidated by summarizing common computational challenges and proposing heuristics to resolve them. While it provides a systematic way to implement SRMs within the BN framework, such generalization can also be used to enhance the functionality of the general-purpose software programs developed for BN as demonstrated by the companion Matlab®-based toolkit BNS-JT. The applicability and efficiency of the proposed framework are demonstrated by numerical examples.

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  • Byun, Ji-Eun & Song, Junho, 2021. "A general framework of Bayesian network for system reliability analysis using junction tree," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021004658
    DOI: 10.1016/j.ress.2021.107952
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    References listed on IDEAS

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    1. Francesco Cavalieri & Paolo Franchin & Pierre Gehl & Dina D’Ayala, 2017. "Bayesian Networks and Infrastructure Systems: Computational and Methodological Challenges," Springer Series in Reliability Engineering, in: Paolo Gardoni (ed.), Risk and Reliability Analysis: Theory and Applications, pages 385-415, Springer.
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    3. Zhou, Jian-Lan & Yu, Ze-Tai & Xiao, Ren-Bin, 2022. "A large-scale group Success Likelihood Index Method to estimate human error probabilities in the railway driving process," Reliability Engineering and System Safety, Elsevier, vol. 228(C).

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