An unsupervised transfer learning bearing fault diagnosis method based on depthwise separable convolution

Abstract Bearings are an essential component of rotating mechanical equipment. Traditional signal processing-based fault diagnosis methods usually require a massive labeled data for training, but bearings generally operate in the equipment under normal fault-free conditions. This paper proposes an improved adaptive batch normalization (AdaBN) transfer learning bearing fault diagnosis method for batch normalization (BN) in traditional deep learning architecture. The AdaBN network preprocesses the raw vibration signals, and then the preprocessed features are input to a depthwise sparable convolution neural model for training. Features are extracted by depthwise convolution and point convolution in the network. AdaBN can freeze all the parameters in the network except the BN layer. Finally, a small amount of labeled data is classified using transfer learning methods. A laboratory data set was used for validation, and the experimental validation showed that the accuracy of the bearing fault diagnosis method using AdaBN reached 85%.