用模糊强化学习增强LSTM，提前1280秒预测核电站故障
A Fuzzy Reinforcement LSTM-based Long-term Prediction Model for Fault Conditions in Nuclear Power Plants
- 融合强化学习与模糊评估的LSTM模型，提升长期预测能力
- 在128步（每步10秒）预测中保持高精度，满足核电故障预警时间需求
- 适合核电健康管理和故障预警场景，可扩展至剩余寿命预测
早期故障检测和及时维护调度能显著降低核电站运行风险，提升操作决策可靠性。因此，亟需构建高效的故障预测与健康管理（PHM）多步预测模型，以预判系统健康状态并推动维护操作。本研究提出一种新型预测模型，将强化学习与长短期记忆（LSTM）神经网络结合，并引入专家模糊评价方法。模型基于CPR1000压水堆模拟模型在主蒸汽管道破裂（MSLB）事故条件下，针对20种不同破裂尺寸的参数数据进行验证，展现出卓越的长期预测能力，可准确预测长达128步（每步10秒，共1280秒）的参数变化，完全满足核电故障预测所需的时间提前量要求。此外，该方法为异常检测、剩余使用寿命预测等PHM应用提供了有效参考方案。
原文摘要 · Abstract (English)
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.
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