Rolling Bearing Fault Diagnosis Method Based on Self-Calibrated Coordinate Attention Mechanism and Multi-Scale Convolutional Neural Network Under Small Samples

Aiming at the problem of poor fault diagnosis performance of rolling bearings under small sample conditions, a novel method based on a self-calibrated coordinate attention mechanism and multi-scale convolutional neural network (SC-MSCNN) is proposed in this article. First, the Markov transition field (MTF) is used to convert the original vibration signals into MTF images with temporal correlation. Then, the self-calibrated coordinate attention mechanism is presented, which obtains feature location information and feature channel information from two directions and locates useful features more accurately. Finally, the SC-MSCNN model is built, and the MTF images are input into the model to complete the classification. The SC-MSCNN model simplifies the structure of the model by adding jump connections, which greatly reduces the number of learnable parameters and thus alleviates the overfitting problem. The approach is demonstrated using two bearing datasets, and the results show that SC-MSCNN can accurately diagnose different fault modes using small samples under given and variable working conditions, which is superior to other popular convolutional neural networks (CNNs).