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A new dynamic Bayesian network model for fault diagnosis of complex systems with high uncertainty in nuclear power plant

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  • Dai, Tao
  • Li, Xiaohan
  • Sui, Yang
  • Zhu, Jiahao
  • Jia, Xiaolong
  • Jin, Yi

Abstract

Nuclear power plant (NPP) is comprised of complex systems with high uncertainty, making fault diagnosis critical for its safe operation. However, traditional dynamic Bayesian network (DBN) model, which rely on precise probabilities, struggles to diagnose faults in such system effectively. This research is to develop a new DBN model to tackle this problem according to the following thought. The regular vine copula and convergent cross mapping (RVC-CCM) method was firstly employed to calculate the structure in DBN model characterizing complex dependency between variables, the cubic spline interpolation and Latin hypercube sampling (CSI-LHS) method was then utilized to calculate the fuzzy observed data for variables under interval type-2 fuzzy environment, and the expectation maximization and whale optimization (EM-WO) method was finally used to learn the conditional probability tables under the interval type-2 fuzzy environment. The model was furthermore applied in a representative case, and the results showed accurate fault diagnosis representative complex system with high uncertainty in NPP. This model enhances the safety and reliability of NPP operation by effectively identifying system faults.

Suggested Citation

  • Dai, Tao & Li, Xiaohan & Sui, Yang & Zhu, Jiahao & Jia, Xiaolong & Jin, Yi, 2026. "A new dynamic Bayesian network model for fault diagnosis of complex systems with high uncertainty in nuclear power plant," Energy, Elsevier, vol. 342(C).
    • Handle: RePEc:eee:energy:v:342:y:2026:i:c:s0360544225051904
    DOI: 10.1016/j.energy.2025.139548
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    References listed on IDEAS

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