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Development and Validation of a Nuclear Power Plant Fault Diagnosis System Based on Deep Learning

Author

Listed:
  • Bing Liu

    (School of Nuclear Science and Technology, University of South China, Hengyang 421000, China)

  • Jichong Lei

    (School of Nuclear Science and Technology, University of South China, Hengyang 421000, China)

  • Jinsen Xie

    (School of Nuclear Science and Technology, University of South China, Hengyang 421000, China)

  • Jianliang Zhou

    (School of Nuclear Science and Technology, University of South China, Hengyang 421000, China)

Abstract

As artificial intelligence technology has progressed, numerous businesses have used intelligent diagnostic technology. This study developed a deep LSTM neural network for a nuclear power plant to defect diagnostics. PCTRAN is used to accomplish data extraction for distinct faults and varied fault degrees of the PCTRAN code, and some essential nuclear parameters are chosen as feature quantities. The training, validation, and test sets are collected using random sampling at a ratio of 7:1:2, and the proper hyperparameters are selected to construct the deep LSTM neural network. The test findings indicate that the fault identification rate of the nuclear power plant fault diagnostic model based on a deep LSTM neural network is more than 99 percent, first validating the applicability of a deep LSTM neural network for a nuclear power plant fault-diagnosis model.

Suggested Citation

  • Bing Liu & Jichong Lei & Jinsen Xie & Jianliang Zhou, 2022. "Development and Validation of a Nuclear Power Plant Fault Diagnosis System Based on Deep Learning," Energies, MDPI, vol. 15(22), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8629-:d:976045
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    References listed on IDEAS

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    1. Sasanka Katreddi & Sujan Kasani & Arvind Thiruvengadam, 2022. "A Review of Applications of Artificial Intelligence in Heavy Duty Trucks," Energies, MDPI, vol. 15(20), pages 1-20, October.
    2. Zirui Wang & Ziqi Zhang & Xu Zhang & Mingxuan Du & Huiting Zhang & Bowen Liu, 2022. "Power System Fault Diagnosis Method Based on Deep Reinforcement Learning," Energies, MDPI, vol. 15(20), pages 1-15, October.
    3. Shrinathan Esakimuthu Pandarakone & Yukio Mizuno & Hisahide Nakamura, 2019. "A Comparative Study between Machine Learning Algorithm and Artificial Intelligence Neural Network in Detecting Minor Bearing Fault of Induction Motors," Energies, MDPI, vol. 12(11), pages 1-14, June.
    4. Jiyang Wu & Qiang Li & Qian Chen & Guangqiang Peng & Jinyu Wang & Qiang Fu & Bo Yang, 2022. "Evaluation, Analysis and Diagnosis for HVDC Transmission System Faults via Knowledge Graph under New Energy Systems Construction: A Critical Review," Energies, MDPI, vol. 15(21), pages 1-20, October.
    5. Younis M. Nsaif & Molla Shahadat Hossain Lipu & Aini Hussain & Afida Ayob & Yushaizad Yusof & Muhammad Ammirrul A. M. Zainuri, 2022. "A New Voltage Based Fault Detection Technique for Distribution Network Connected to Photovoltaic Sources Using Variational Mode Decomposition Integrated Ensemble Bagged Trees Approach," Energies, MDPI, vol. 15(20), pages 1-20, October.
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    Cited by:

    1. 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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