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A fault diagnosis method for small pressurized water reactors based on long short-term memory networks

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  • Wang, Pengfei
  • Zhang, Jiaxuan
  • Wan, Jiashuang
  • Wu, Shifa

Abstract

This paper proposes a sensor and actuator fault diagnosis method for small pressurized water reactors (SPWRs), with an innovative labeled fault dictionary established to map complex fault modes, using long short-term memory (LSTM) networks. It can directly learn features from multivariable time-series data and capture long-term dependencies through the cyclic behavior and gate mechanism of LSTM to realize the end-to-end fault diagnosis of SPWRs. Experimental results on a SPWR fault dataset show that the method can effectively diagnose the location, type, and extent of sensor and actuator faults from raw time-series signals with an average accuracy of 92.06% and outperforms three other widely-used fault diagnosis methods. Furthermore, the diagnosis results on the SPWR fault dataset injected with different noise signals demonstrate the strong noise immunity capability of the established LSTM network. Therefore, the proposed method is expected to achieve satisfactory fault diagnosis performances in actual operating environments of SPWRs.

Suggested Citation

  • Wang, Pengfei & Zhang, Jiaxuan & Wan, Jiashuang & Wu, Shifa, 2022. "A fault diagnosis method for small pressurized water reactors based on long short-term memory networks," Energy, Elsevier, vol. 239(PC).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pc:s0360544221025469
    DOI: 10.1016/j.energy.2021.122298
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    3. Zhe Dong & Zhonghua Cheng & Yunlong Zhu & Xiaojin Huang & Yujie Dong & Zuoyi Zhang, 2023. "Review on the Recent Progress in Nuclear Plant Dynamical Modeling and Control," Energies, MDPI, vol. 16(3), pages 1-19, February.
    4. Jinrui Nan & Bo Deng & Wanke Cao & Jianjun Hu & Yuhua Chang & Yili Cai & Zhiwei Zhong, 2022. "Big Data-Based Early Fault Warning of Batteries Combining Short-Text Mining and Grey Correlation," Energies, MDPI, vol. 15(15), pages 1-19, July.
    5. Hui, Jiuwu, 2024. "Coordinated discrete-time super-twisting sliding mode controller coupled with time-delay estimator for PWR-based nuclear steam supply system," Energy, Elsevier, vol. 301(C).
    6. Yang, Xilian & Zhao, Qunfei & Wang, Yuzhang & Cheng, Kanru, 2023. "Fault signal reconstruction for multi-sensors in gas turbine control systems based on prior knowledge from time series representation," Energy, Elsevier, vol. 262(PA).
    7. Hui, Jiuwu & Lee, Yi-Kuen & Yuan, Jingqi, 2023. "ESO-based adaptive event-triggered load following control design for a pressurized water reactor with samarium–promethium dynamics," Energy, Elsevier, vol. 271(C).
    8. Attallah, Omneya & Ibrahim, Rania A. & Zakzouk, Nahla E., 2023. "CAD system for inter-turn fault diagnosis of offshore wind turbines via multi-CNNs & feature selection," Renewable Energy, Elsevier, vol. 203(C), pages 870-880.
    9. Dong, Zhe & Li, Bowen & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2022. "Power-pressure coordinated control of modular high temperature gas-cooled reactors," Energy, Elsevier, vol. 252(C).
    10. 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).
    11. Dao, Fang & Zeng, Yun & Qian, Jing, 2024. "Fault diagnosis of hydro-turbine via the incorporation of bayesian algorithm optimized CNN-LSTM neural network," Energy, Elsevier, vol. 290(C).
    12. Zhou, Shiqi & Lin, Meng & Huang, Shilong & Xiao, Kai, 2024. "Open set compound fault recognition method for nuclear power plant based on label mask weighted prototype learning," Applied Energy, Elsevier, vol. 369(C).

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