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Research on an Intelligent Fault Diagnosis Method for Small Modular Reactors

Author

Listed:
  • Changan Ren

    (School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
    School of Computer Science and Engineering, Hunan Institute of Technology, Hengyang 421002, China
    These authors contributed equally to this work.)

  • Jichong Lei

    (School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
    School of Safe and Management Engineering, Hunan Institute of Technology, Hengyang 421002, China
    Key Lab of Advanced Nuclear Energy Design and Safety, Ministry of Education, Hengyang 421001, China
    These authors contributed equally to this work.)

  • Jie Liu

    (School of Computing/Software, University of South China, Hengyang 421001, China)

  • Jun Hong

    (School of Safe and Management Engineering, Hunan Institute of Technology, Hengyang 421002, China)

  • Hong Hu

    (School of Safe and Management Engineering, Hunan Institute of Technology, Hengyang 421002, China)

  • Xiaoyong Fang

    (School of Safe and Management Engineering, Hunan Institute of Technology, Hengyang 421002, China)

  • Cannan Yi

    (School of Safe and Management Engineering, Hunan Institute of Technology, Hengyang 421002, China)

  • Zhiqiang Peng

    (School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
    School of Safe and Management Engineering, Hunan Institute of Technology, Hengyang 421002, China
    Key Lab of Advanced Nuclear Energy Design and Safety, Ministry of Education, Hengyang 421001, China)

  • Xiaohua Yang

    (School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
    School of Computing/Software, University of South China, Hengyang 421001, China)

  • Tao Yu

    (School of Nuclear Science and Technology, University of South China, Hengyang 421001, China
    Key Lab of Advanced Nuclear Energy Design and Safety, Ministry of Education, Hengyang 421001, China)

Abstract

Small modular reactors (SMRs) are currently advancing towards increased degrees of automation and intelligence, with intelligent control emerging as a prominent trend in SMR development. SMRs exhibit significant variations in design specifications and safety auxiliary system design as compared to conventional commercial nuclear power reactors. Consequently, defect diagnostic techniques that rely on commercial nuclear power plants are not appropriate for SMRs. This study designed a defect detection system for the System-integrated Modular Advanced ReacTor SMR by utilizing the PCTRAN/SMR V1.0 software and a deep learning neural network structure. Through the comparison of several neural network designs, it was discovered that the CNN-BiLSTM model, which utilizes bidirectional data processing, obtained a fault diagnostic accuracy of 97.33%. This result confirms the accuracy and effectiveness of the fault diagnosis system. This strongly supports the eventual implementation of autonomous control for SMRs.

Suggested Citation

  • Changan Ren & Jichong Lei & Jie Liu & Jun Hong & Hong Hu & Xiaoyong Fang & Cannan Yi & Zhiqiang Peng & Xiaohua Yang & Tao Yu, 2024. "Research on an Intelligent Fault Diagnosis Method for Small Modular Reactors," Energies, MDPI, vol. 17(16), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:4049-:d:1456748
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    References listed on IDEAS

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    1. Ahmad Waleed Salehi & Shakir Khan & Gaurav Gupta & Bayan Ibrahimm Alabduallah & Abrar Almjally & Hadeel Alsolai & Tamanna Siddiqui & Adel Mellit, 2023. "A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope," Sustainability, MDPI, vol. 15(7), pages 1-28, March.
    2. Elaheh Shobeiri & Filippo Genco & Daniel Hoornweg & Akira Tokuhiro, 2023. "Small Modular Reactor Deployment and Obstacles to Be Overcome," Energies, MDPI, vol. 16(8), pages 1-19, April.
    3. Xie, Wanni & Atherton, John & Bai, Jiaru & Farazi, Feroz & Mosbach, Sebastian & Akroyd, Jethro & Kraft, Markus, 2024. "A nuclear future? Small Modular Reactors in a carbon tax-driven transition to clean energy," Applied Energy, Elsevier, vol. 364(C).
    4. Haixia Gu & Gaojun Liu & Jixue Li & Hongyun Xie & Hanguan Wen, 2023. "A Framework Based on Deep Learning for Predicting Multiple Safety-Critical Parameter Trends in Nuclear Power Plants," Sustainability, MDPI, vol. 15(7), pages 1-15, April.
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    Cited by:

    1. Diego Rodriguez-Obando & Javier Rosero-García & Esteban Rosero, 2024. "Dynamic Data-Driven Deterioration Model for Sugarcane Shredder Hammers Oriented to Lifetime Extension," Mathematics, MDPI, vol. 12(22), pages 1-22, November.

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