A rolling bearing fault diagnosis method based on multi-scale knowledge distillation and continual learning

To alleviate the catastrophic forgetting problem caused by the single-task bearing fault diagnosis method under different working conditions, a rolling bearing fault diagnosis method based on multi-scale knowledge distillation and continual learning (CL-MSKD) was proposed. The one-dimensional convolutional neural network was used as the main framework of CL-MSKD, and the cosine normalization layer was used as a multi-task shared classifier. The model knowledge was preserved and transmitted through the knowledge distillation of label and feature scales. CL-MSKD can diagnose bearing faults under different working conditions with a unified structure network model, continuously learn and save knowledge through a knowledge compression method, and finally alleviate the catastrophic forgetting problem in the incremental stage, and improve the bearing fault diagnosis performance under cross-working conditions. The experiment shows that CL-MSKD can effectively alleviate catastrophic amnesia and maintain good diagnostic effect. In the case of large differences in task environments, the accuracy index can still reach 97.09%, which has better stability and higher precision than other incremental methods. © 2024 Chinese Vibration Engineering Society. All rights reserved.