基于双通道特征融合的轴承故障诊断方法

Bearing fault diagnosis method based on dual-channel feature fusion

基于卷积神经网络(Convolution Neural Network,CNN)的智能诊断方法在轴承故障诊断中应用广泛,但是大多数诊断模型以单源信息输入为主,这将影响基于CNN的故障诊断准确性和可靠性。针对这个问题,文章提出一种基于双通道特征融合的滚动轴承故障诊断方法。首先利用多重Q因子连续Gabor小波变换(Multiple Q-factor Continuous Gabor Wavelet Transform,CMQGWT)和快速谱相干(Fast Spectral Coherence,Fast-SC)分别构造滚动轴承振动信号的时频分析图;然后搭建1个具有双输入通道的CNN网络模型,通过特征融合层将各个通道提取的深度时频特征融合成1个新的特征;最后利用分类器输出诊断结果。在高速列车滚动轴承单故障和复合故障的分类识别试验中,较之于单输入通道的CNN模型,该模型具有更高的诊断准确性和鲁棒性。

Intelligent diagnosis method based on convolution neural network(CNN)has been widely used in bearing fault diagnosis.However,most existing diagnostic models rely on single-source information inputs,limiting their accuracy and reliability.To solve this limitation,this paper presents a rolling bearing fault diagnosis method based on dual-channel feature fusion.Firstly,the time-frequency analysis diagrams of rolling bearing vibration signals were constructed by using multiple Q-factor continuous Gabor wavelet transform(CMQGWT)and fast spectral coherence(Fast-SC),respectively.Subsequently,a CNN model with dual input channels was constructed,allowing for the fusion of deep time-frequency features extracted from each channel into a new feature at a feature fusion layer.Finally,the diagnosis results were output using a classifier.Through classification and recognition experiments involving single and compound faults in rolling bearings for high-speed trains,compared with the CNN model with a single input channel,the proposed model demonstrates superior diagnostic accuracy and robustness.