A transfer learning-based fault diagnosis method for rolling bearings with variable operating conditions

Abstract

Aiming at the problem that fault feature information cannot be completely extracted and it is difficult to obtain a large amount of sample data for fault labeling in real production life, we propose a transfer learning-based fault diagnosis method for rolling bearings with variable operating conditions. First, in order to make up for the single limitation of the feature extraction of the original vibration signal, a new feature signal is formed by fusing the time domain features on the basis of the original vibration signal, which is used as the input of the model, and a lightweight one-dimensional convolutional neural network(1d-CNN) is constructed, and an efficient channel attention mechanism is introduced to extract the fault features, so as to get the source domain diagnostic model. Then, according to the idea of transfer learning, the vibration signals under different working conditions are input into the fine-tuned model to realize the rolling bearing fault diagnosis under multiple working conditions. The results show that the method can realize migration under different working conditions and accurately and efficiently realize rolling bearing fault diagnosis.