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Designing a Bayesian network for preventive maintenance from expert opinions in a rapid and reliable way

Author

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  • Celeux, G.
  • Corset, F.
  • Lannoy, A.
  • Ricard, B.

Abstract

In this study, a Bayesian Network (BN) is considered to represent a nuclear plant mechanical system degradation. It describes a causal representation of the phenomena involved in the degradation process. Inference from such a BN needs to specify a great number of marginal and conditional probabilities. As, in the present context, information is based essentially on expert knowledge, this task becomes very complex and rapidly impossible. We present a solution, which consists of considering the BN as a log-linear model on which simplification constraints are assumed. This approach results in a considerable decrease in the number of probabilities to be given by experts. In addition, we give some simple rules to choose the most reliable probabilities. We show that making use of those rules allows to check the consistency of the derived probabilities. Moreover, we propose a feedback procedure to eliminate inconsistent probabilities. Finally, the derived probabilities that we propose to solve the equations involved in a realistic Bayesian network are expected to be reliable. The resulting methodology to design a significant and powerful BN is applied to a reactor coolant sub-component in EDF Nuclear plants in an illustrative purpose.

Suggested Citation

  • Celeux, G. & Corset, F. & Lannoy, A. & Ricard, B., 2006. "Designing a Bayesian network for preventive maintenance from expert opinions in a rapid and reliable way," Reliability Engineering and System Safety, Elsevier, vol. 91(7), pages 849-856.
    • Handle: RePEc:eee:reensy:v:91:y:2006:i:7:p:849-856
    DOI: 10.1016/j.ress.2005.08.007
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    Citations

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    Cited by:

    1. Muller, Alexandre & Suhner, Marie-Christine & Iung, Benoît, 2008. "Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system," Reliability Engineering and System Safety, Elsevier, vol. 93(2), pages 234-253.
    2. Gambelli, Danilo & Alberti, Francesca & Solfanelli, Francesco & Vairo, Daniela & Zanoli, Raffaele, 2017. "Third generation algae biofuels in Italy by 2030: A scenario analysis using Bayesian networks," Energy Policy, Elsevier, vol. 103(C), pages 165-178.
    3. Chemweno, Peter & Pintelon, Liliane & Van Horenbeek, Adriaan & Muchiri, Peter, 2015. "Development of a risk assessment selection methodology for asset maintenance decision making: An analytic network process (ANP) approach," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 663-676.
    4. Rafic Faddoul & Wassim Raphael & Abdul-Hamid Soubra & Alaa Chateauneuf, 2013. "Incorporating Bayesian networks in Markov Decision Processes," Post-Print hal-01006963, HAL.
    5. Chrétien, Stéphane & Corset, Franck, 2016. "A lower bound on the expected optimal value of certain random linear programs and application to shortest paths in Directed Acyclic Graphs and reliability," Statistics & Probability Letters, Elsevier, vol. 117(C), pages 221-230.
    6. Zheng, Yi-Xuan & Xiahou, Tangfan & Liu, Yu & Xie, Chaoyang, 2021. "Structure function learning of hierarchical multi-state systems with incomplete observation sequences," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    7. Norrington, Lisa & Quigley, John & Russell, Ashley & Van der Meer, Robert, 2008. "Modelling the reliability of search and rescue operations with Bayesian Belief Networks," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 940-949.

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