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Fault Diagnosis Techniques for Nuclear Power Plants: A Review from the Artificial Intelligence Perspective

Author

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  • Ben Qi

    (Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China)

  • Jingang Liang

    (Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China)

  • Jiejuan Tong

    (Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China)

Abstract

Fault diagnosis plays an important role in complex and safety-critical systems such as nuclear power plants (NPPs). With the development of artificial intelligence (AI), extensive research has been carried out for fast and efficient fault diagnosis based on intelligent methods. This paper presents a review of various AI-based system-level fault diagnosis methods for NPPs. We first discuss the development history of AI. Based on this exposition, AI-based fault diagnosis techniques are classified into knowledge-driven and data-driven approaches. For knowledge-driven methods, we discuss both the early if–then-based fault diagnosis techniques and the current new theory-based ones. The principles, application, and comparative analysis of the representative methods are systematically described. For data-driven strategies, we discuss single-algorithm-based techniques such as ANN, SVM, PCA, DT, and clustering, as well as hybrid techniques that combine algorithms together. The advantages and disadvantages of both knowledge-driven and data-driven methods are compared, illustrating the tendency to combine the two approaches. Finally, we provide some possible future research directions and suggestions.

Suggested Citation

  • Ben Qi & Jingang Liang & Jiejuan Tong, 2023. "Fault Diagnosis Techniques for Nuclear Power Plants: A Review from the Artificial Intelligence Perspective," Energies, MDPI, vol. 16(4), pages 1-27, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1850-:d:1067342
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    References listed on IDEAS

    as
    1. Li, Jiangkuan & Lin, Meng & Li, Yankai & Wang, Xu, 2022. "Transfer learning network for nuclear power plant fault diagnosis with unlabeled data under varying operating conditions," Energy, Elsevier, vol. 254(PB).
    2. Xinwei Cong & Caiping Zhang & Jiuchun Jiang & Weige Zhang & Yan Jiang & Linjing Zhang, 2021. "A Comprehensive Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 14(5), pages 1-21, February.
    3. Roberto Pierdicca & Marina Paolanti & Andrea Felicetti & Fabio Piccinini & Primo Zingaretti, 2020. "Automatic Faults Detection of Photovoltaic Farms: solAIr, a Deep Learning-Based System for Thermal Images," Energies, MDPI, vol. 13(24), pages 1-17, December.
    4. Tyler J. VanderWeele & James M. Robins, 2010. "Signed directed acyclic graphs for causal inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 111-127, January.
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    Cited by:

    1. Xingyu Xiao & Ben Qi & Jingang Liang & Jiejuan Tong & Qing Deng & Peng Chen, 2023. "Enhancing LOCA Breach Size Diagnosis with Fundamental Deep Learning Models and Optimized Dataset Construction," Energies, MDPI, vol. 17(1), pages 1-20, December.
    2. Zhiqiang Peng & Jichong Lei & Zining Ni & Tao Yu & Jinsen Xie & Jun Hong & Hong Hu, 2024. "Research on Data-Driven Methods for Solving High-Dimensional Neutron Transport Equations," Energies, MDPI, vol. 17(16), pages 1-11, August.
    3. Xingyu Xiao & Jingang Liang & Jiejuan Tong & Haitao Wang, 2024. "Emergency Decision Support Techniques for Nuclear Power Plants: Current State, Challenges, and Future Trends," Energies, MDPI, vol. 17(10), pages 1-35, May.
    4. Chenyang Lai & Ibrahim Ahmed & Enrico Zio & Wei Li & Yiwang Zhang & Wenqing Yao & Juan Chen, 2024. "A Multistage Physics-Informed Neural Network for Fault Detection in Regulating Valves of Nuclear Power Plants," Energies, MDPI, vol. 17(11), pages 1-23, May.

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