Fatigue Detection Algorithm for Nuclear Power Plant Operators Based on Random Forest and Back Propagation Neural Networks - Yuhang Jiang; Junsong Li; Yu Zhang (Mathematics)
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.