An Intelligent Hybrid Bearing Fault Diagnosis Method Based on Transformer and Domain Adaptation

Abstract

Deep-Learning (DL) methods have been successfully applied in the bearing fault diagnosis field. However, previous methods mainly focus on two assumptions: 1) training (source) and testing (target) data are sufficient with labels; 2) the distribution of the training and testing data are identical, which are obviously easy to be violated in the industry. Recently, domain adaptation has been successfully adopted to tackle with problem of insufficient labeled data and distribution discrepancies. However, most of the previous methods only focus on minimizing global distribution discrepancies without considering the conditional distribution. To remedy the limitation, a novel hybrid method combing Simplified Convolutional vision transformer and Domain Adaptation (SCDA) is proposed for aligning the distribution across global and subdomains in source and target data. Firstly, a convolutional vision transformer is utilized for extracting bearing feature with better generalization ability. Then, the adversarial learning and local maximum mean discrepancy (LMMD) is utilized for the global domain adaptation and subdomain adaptation to extract domain invariant features. Finally, the classifier trained by the domain invariant features is used for the fault diagnosis of target data. The proposed method is evaluated through the Paderborn University (PU) bearing dataset with the result showing its effectiveness and transferability.