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Fatigue Detection Algorithm for Nuclear Power Plant Operators Based on Random Forest and Back Propagation Neural Networks

Author

Listed:
  • Yuhang Jiang

    (Department of Computer Science & Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Junsong Li

    (Department of Computer Science & Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Yu Zhang

    (Department of Computer Science & Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China)

Abstract

This article proposes a fatigue detection algorithm for nuclear power plant control room operators based on random forest and BP neural networks, specifically targeting the control room scenario. This algorithm is capable of detecting fatigue-related operations in a timely manner, which is crucial for ensuring the safe operation of nuclear power plants. First, the random forest algorithm is used to classify the feature data according to different scenarios. Second, the data are distributed to different back propagation neural networks for prediction based on the scenario. Finally, experimental validation is conducted using a reactor simulation system. The results show that the algorithm achieves a recognition accuracy of 0.82, an accuracy of 0.69, a recall rate of 0.64, and an F1-Score of 0.66, indicating that the proposed algorithm has practical value for detecting operator fatigue in nuclear power plants. Compared to physiological data-based detection methods, it is simple, convenient, cost-effective, and does not interfere with operators.

Suggested Citation

  • Yuhang Jiang & Junsong Li & Yu Zhang, 2025. "Fatigue Detection Algorithm for Nuclear Power Plant Operators Based on Random Forest and Back Propagation Neural Networks," Mathematics, MDPI, vol. 13(5), pages 1-17, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:5:p:774-:d:1600470
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    References listed on IDEAS

    as
    1. Xubao Liu & Yuhang Pan & Ying Yan & Yonghao Wang & Ping Zhou, 2022. "Adaptive BP Network Prediction Method for Ground Surface Roughness with High-Dimensional Parameters," Mathematics, MDPI, vol. 10(15), pages 1-18, August.
    2. Yiheng Li & Weidong Chen, 2020. "A Comparative Performance Assessment of Ensemble Learning for Credit Scoring," Mathematics, MDPI, vol. 8(10), pages 1-19, October.
    3. Yunjie Xiang & Rong Hu & Yong Xu & Chih-Yu Hsu & Congliu Du, 2023. "Gaussian Weighted Eye State Determination for Driving Fatigue Detection," Mathematics, MDPI, vol. 11(9), pages 1-24, April.
    4. Mohammad Hijji & Hikmat Yar & Fath U Min Ullah & Mohammed M. Alwakeel & Rafika Harrabi & Fahad Aradah & Faouzi Alaya Cheikh & Khan Muhammad & Muhammad Sajjad, 2023. "FADS: An Intelligent Fatigue and Age Detection System," Mathematics, MDPI, vol. 11(5), pages 1-16, February.
    5. Yi Wang & Zhengxiang He & Liguan Wang, 2021. "Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines," Mathematics, MDPI, vol. 9(22), pages 1-14, November.
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