摘要
为提高核电厂瞬态工况下参数预测和故障诊断的准确性和实时性,本研究采用长短期记忆(LSTM)神经网络模型进行预测和诊断。通过生成并随机化故障情景,减少预测模型对特定模式的依赖,提高其在未知故障情景下的泛化能力。研究结合沙普利加性解释性(SHAP)方法,对预测模型的参数预测结果进行解释性分析,评估不同输入特征对模型预测性能的影响,并验证该预测模型在传感器故障和数据传输错误情况下的预测准确性。此外,针对含有不同噪声水平的瞬态参数进行故障诊断,验证故障诊断模型的鲁棒性。结果表明,LSTM神经网络模型在预测和诊断方面具有较高的精度,即使在传感器故障、数据传输有误以及数据含有噪声情况下仍表现出色。本研究提出的方法能够提升核电厂运行安全和稳定性,为事故工况下的安全性提供有效技术支持。
To improve the accuracy and real-time performance of parameter prediction and fault diagnosis under transient conditions in nuclear power plants,this study employs a Long Short-Term Memory(LSTM)neural network model for prediction and diagnosis.By generating and randomizing fault scenarios,the model’s dependence on specific patterns is reduced,and its generalization capability in unknown fault situations is enhanced.The study integrates SHAP(SHapley Additive exPlanations)to conduct interpretability analysis on the parameter prediction results,evaluates the impact of different input features on the model’s predictive performance,and verifies its effectiveness under sensor failures and data transmission errors.Furthermore,fault diagnosis is performed on transient parameters with different noise levels to validate the model's robustness.The results demonstrate that the LSTM model achieves high accuracy in both prediction and diagnosis,and it maintains excellent performance even under sensor failures,data transmission errors,and noisy data.The method proposed in this study can improve the safety and stability of nuclear power plant operation and provide effective technical support for safety under accident conditions.
作者
刘涛
谢金森
Liu Tao;Xie Jinsen(School of Resources,Environment and Safety Engineering,University of South China,Hengyang,Hunan,421001,China;Nuclear Energy and Nuclear Technology Engineering Virtual Simulation Experiment Teaching Center,University of South China,Hengyang,Hunan,421001,China;School of Nuclear Science and Technology,University of South China,Hengyang,Hunan,421001,China)
出处
《核动力工程》
北大核心
2025年第2期230-238,共9页
Nuclear Power Engineering
基金
国家自然科学基金(U2267207)
湖南省自然科学基金(2022JJ30481)。
关键词
核电厂
瞬态参数预测
故障诊断
LSTM神经网络
沙普利加性解释性(SHAP)方法
Nuclear power plant
Transient parameter prediction
Fault diagnosis
Long shortterm memory neural network
SHapley Additive exPlanations(SHAP)method
