Multi-Scale Rolling Bearing Fault Diagnosis Method Based on Transfer Learning

Abstract

Confronting the challenge of identifying unknown fault types in rolling bearing fault diagnosis, this study introduces a multi-scale bearing fault diagnosis method based on transfer learning. Initially, a multi-scale feature extraction network, MBDCNet, is constructed. This network, by integrating the features of vibration signals at multiple scales, is dedicated to capturing key information within bearing vibration signals. Innovatively, this study replaces traditional convolution with dynamic convolution in MBDCNet, aiming to enhance the model’s flexibility and adaptability. Furthermore, the study implements pre-training and transfer learning strategies to maximally extract latent knowledge from source domain data. By optimizing the loss function and fine-tuning the learning rate, the robustness and generalization ability of the model in the target domain are significantly improved. The proposed method is validated on bearing datasets provided by Case Western Reserve University and Jiangnan University. The experimental results demonstrate high accuracy in most diagnostic tasks, achieving optimal average accuracy on both datasets, thus verifying the stability and robustness of our approach in various diagnostic tasks. This offers a reliable research direction in terms of enhancing the reliability of industrial equipment, especially in the field of bearing fault diagnosis.

Keywords

• fault diagnosis
• transfer learning
• dynamic convolution
• loss function