Fault diagnosis method for bearings under variable working conditions based on transfer relation network

Abstract In order to achieve robust fault diagnosis under varying conditions with limited labeled data, this study combines metric-based meta-learning with feature-based domain adaptation. It introduces a new approach for variable-condition bearing fault diagnosis using transfer relation networks. To enhance the network’s ability to generalize across different domains, the paper integrates local maximum mean discrepancy (LMMD) into the relation network architecture. LMMD aligns the data distributions of various classes between the source and target domains, effectively addressing distributional differences and improving model generalization. To accurately and swiftly extract meaningful fault features, the study proposes a lightweight feature extraction module based on Shuffle Attention (SA). This module employs depth-wise separable convolutions for efficiency and integrates SA after each convolutional layer to bolster feature representation. Finally, experiments on two bearing datasets under varying conditions validate the efficacy and superiority of the proposed model over alternative methods.