摘要
针对核电厂水泵共性的异常振动、转子部件摩擦与磨损等故障模式,利用水泵最容易获取的泵壳加速度信号的频域数据为输入,提出了一种结合卷积神经网络和注意力网络的频域数据注意力机制方法,并建立了核电厂水泵故障模式识别模型。研究结果表明:相对于传统方法,利用频域数据作为输入、基于频域数据注意力网络算法建立的水泵故障模式识别模型输入的数据长度更短,能够有效提升模型训练的效率,该故障模式识别模型在测试集上的故障模式识别准确率达到100%,优于其他基于深度学习算法建立的故障诊断模型,证明了本文提出方法的优势。
In view of the common fault modes of nuclear power plant pump,such as abnormal vibration,friction and abrasion of rotor parts,etc.,this paper uses frequency domain data of the acceleration signal on pump shell which is easiest to be obtained as input,proposes a new method for frequency-domain data attention mechanism which combines convolutional neural network and attention network,and establishes the recognition model of fault mode of nuclear power plant water pump.The results show that:Compared with the traditional methods,the water pump fault pattern recognition model based on frequency domain data as input and based on frequency domain data attention network algorithm has a shorter input data length and can effectively improve the efficiency of model training.The fault pattern recognition accuracy of the fault pattern recognition model on the test set is 100%,which is better than other fault diagnosis models based on deep learning algorithm,which proves the advantages of the method proposed in this paper.
作者
刘子铭
罗能
艾琼
Liu Ziming;Luo Neng;Ai Qiong(Chengdu University of Information Technology,Chengdu,610225,China;Nuclear Power Institute of China,Chengdu,610213,China)
出处
《核动力工程》
EI
CAS
CSCD
北大核心
2021年第6期203-208,共6页
Nuclear Power Engineering
关键词
核电厂水泵
频域数据
注意力机制
故障模式识别
Nuclear power plant water pump
Frequency domain data
Attention mechanism
Fault pattern recognition
