Rolling bearing fault diagnosis based on time-frequency representation and multi-scale feature learning
A rolling bearing fault diagnosis method based on multi-scale depthwise separable convolution (DSC) and a convolutional neural network-Transformer hybrid model (CNN-Transformer) is proposed to address the non-stationarity of fault signals and the difficulty of jointly capturing local and global features. First, continu...