基于Inception-DLSTM双通道的滚动轴承故障诊断方法

Rolling bearing fault diagnosis method based on Inception-DLSTM dual channel

卷积神经网络(CNN)对空间特征具有敏感性,而Inception相比CNN具备多尺度提取特征优势;长短时记忆网络(LSTM)对时间特征具有敏感性,而深层长短时记忆网络(DLSTM)比LSTM具备更深层次提取特征优势。为了多尺度充分提取滚动轴承振动信号在空间和时间上的特征,实现滚动轴承故障诊断,提出了一种Inception通道和DLSTM通道结合的Inception-DLSTM双通道滚动轴承故障诊断模型。对于Inception通道,把轴承振动信号经过小波变换生成的时频图作为输入,利用Inception网络多尺度提取时频图的空间特征信息;对于DLSTM通道,直接把轴承振动信号作为输入,利用DLSTM网络充分提取信号的时间特征信息。然后把两个通道输出的特征信息连接成一个时空特征向量,最后利用分类器进行轴承故障诊断识别。对轴承故障数据进行对比实验可得,Inception-DLSTM双通道的故障识别准确率可达100%,具备良好的故障诊断和特征提取能力。

Convolution neural network(CNN) is sensitive to spatial features,while Inception has the advantage of multi-scale feature extraction compared with CNN,long-short-term memory network(LSTM) is sensitive to temporal features,and deep short-term memory network(DLSTM) has deeper feature extraction advantages than LSTM.In order to fully extract the spatial and temporal characteristics of rolling bearing vibration signals in multi-scale,a dual-channel rolling bearing fault diagnosis model Inception-DLSTM based on the combination of Inception channel and DLSTM channel is proposed.For the Inception channel,the time-frequency diagram generated by the wavelet transform of the bearing vibration signal is used as the input,and the multi-scale Inception network is used to extract the spatial feature information of the time-frequency diagram;for the DLSTM channel,the bearing vibration signal is directly taken as the input,and the DLSTM network is used to fully extract the time feature information of the signal.Then the feature information output from the two channels is connected into a spatio-temporal feature vector,and finally the classifier is used to diagnose and identify the bearing fault.Comparing the bearing fault data can be obtained,and the fault identification accuracy of the Inception-DLSTM dual channel can reach 100%,and has good fault diagnosis and feature extraction capabilities.