摘要
目前,核电站设备传感器每天都会产生大量的监测数据,但这些数据的利用程度较低,对于利用监测数据进行核电站重要设备的故障预测研究还处于探索阶段。针对这种情况,本文以核电站主给水泵作为研究对象,将表征主给水泵运行状态的各类监测数据进行预处理和降维,进而通过多个选定的机器学习模型预测设备在未来是否会发生故障。通过对预测模型的效果评估发现,长短时记忆网络模型(LSTM模型)具有较好的预测精度。当模型预测结果超过阈值时发出预警信息,提醒核电站运维人员加强关注,及时采取故障诊断和维修措施,以有效防止因设备的突然故障停运造成较为严重的后果,保证核电站的安全和经济运行。
At present,nuclear power plant equipment sensors generate a large amount of monitoring data every day,but the utilization of these data is relatively low.Research on using monitoring data for fault prediction of important equipment in nuclear power plants is still in the exploratory stage.In response to this situation,this article takes the main feedwater pump of nuclear power plants as the research object,preprocesses the operation status data,reduces the various monitoring data dimensions,and then predicts whether the equipment will malfunction in the future through multiple selected machine learning models.The selected models are Linear model,support vector machine(SVM)model,and LSTM model.For the fault diagnosis of the main feedwater pump,the discrimination criterion is that the vibration signal of the feedwater pump bearing exceeds the threshold.When the predicted results of the model exceed the threshold,an early warning message is issued to remind the operation and maintenance personnel of the nuclear power plant to strengthen their attention,take timely fault diagnosis and maintenance measures,and prevent serious consequences caused by sudden equipment failure and shutdown,which will affect the safe operation of nuclear power plants.Specifically,this study utilized the operating parameters of the main feedwater pump during operation,which are more than ten parameters other than the vibration signals of the feedwater pump bearings.Through a predictive model,the vibration situation of the feedwater pump bearings was predicted,and the fault status of the main feedwater pump was determined based on the vibration prediction signals to evaluate whether the equipment can operate for a long time.After preprocessing and feature selection,the selected dataset was trained and validated against traditional data processing models and currently popular deep learning models.After evaluation,it is found that the deep learning model LSTM has more accurate prediction results,with mean square error and goodness of fit meeting the required range and could better fit the actual operation of the equipment.At the same time,by using the ridge regression algorithm,the parameters that have a significant impact on the normal operation of the main feedwater pump can be analyzed.In the actual work of the equipment,it can help operation and maintenance personnel find the cause of faults and improve the quality of equipment operation and maintenance.The historical data used in this study mainly cover common typical faults of the main feedwater pump.In the future,it is necessary to continue collecting other types of fault data for model training and validation,so that the model can cover all possible faults that may occur in the main feedwater pump.Overall,the research prospects for fault prediction of such large-scale equipment are very broad.
作者
张国辉
骆志平
ZHANG Guohui;LUO Zhiping(China Institute of Atomic Energy,Beijing 102413,China)
出处
《原子能科学技术》
EI
CAS
CSCD
北大核心
2024年第9期1982-1988,共7页
Atomic Energy Science and Technology
关键词
核电站
主给水泵
机器学习
故障预测
拟合
nuclear power plant
main feedwater pump
machine learning
fault prediction
fitting
