A Fuzzy Reinforcement LSTM-based Long-term Prediction Model for Fault Conditions in Nuclear Power Plants
This addresses fault prognostics for nuclear power plant operators to enhance safety and maintenance scheduling, though it is incremental as it builds on existing methods like LSTM and reinforcement learning.
The study tackled fault prediction in nuclear power plants by developing a model combining reinforcement learning, LSTM, and fuzzy evaluation, which accurately forecasted parameter changes up to 128 steps ahead (1280 seconds) using data from a CPR1000 reactor simulation.
Early fault detection and timely maintenance scheduling can significantly mitigate operational risks in NPPs and enhance the reliability of operator decision-making. Therefore, it is necessary to develop an efficient Prognostics and Health Management (PHM) multi-step prediction model for predicting of system health status and prompt execution of maintenance operations. In this study, we propose a novel predictive model that integrates reinforcement learning with Long Short-Term Memory (LSTM) neural networks and the Expert Fuzzy Evaluation Method. The model is validated using parameter data for 20 different breach sizes in the Main Steam Line Break (MSLB) accident condition of the CPR1000 pressurized water reactor simulation model and it demonstrates a remarkable capability in accurately forecasting NPP parameter changes up to 128 steps ahead (with a time interval of 10 seconds per step, i.e., 1280 seconds), thereby satisfying the temporal advance requirement for fault prognostics in NPPs. Furthermore, this method provides an effective reference solution for PHM applications such as anomaly detection and remaining useful life prediction.