Normalization-Guided and Gradient-Weighted Unsupervised Domain Adaptation Network for Transfer Diagnosis of Rolling Bearing Faults Under Class Imbalance

Abstract

Transfer learning has garnered significant interest in the field of bearing fault diagnosis under varying operational conditions due to its robust generalization capabilities. However, real-world diagnostic scenarios frequently encounter data imbalances, which complicates the learning of the classification boundary for the minority class within the diagnostic model. To address this challenge, we propose a normalization-guided and gradient-weighted unsupervised domain adaptation network (NG-UDAN) for intelligent bearing fault diagnosis, aimed at tackling inter-domain feature shifts and intra-domain category imbalances. Firstly, the proposed network integrates a residual feature extractor with the Domain Normalization (DN) module to enhance domain-invariant feature extraction. Subsequently, the Local Maximum Mean Discrepancy (LMMD) loss is utilized to minimize the conditional distributional differences between the source and target domains. Finally, the Gradient-Weighted Focal Loss (GWFL) is specifically designed to address the issue of class imbalance. Experiments conducted across three imbalanced scenarios using the Case Western Reserve University (CWRU) and Paderborn University (PU) datasets demonstrate that NG-UDAN is effective in both single-source and mixed-source domain adaptation. Furthermore, comparisons with alternative methods validate the superiority of this approach in managing class imbalances under varying working conditions.

Keywords

• fault diagnosis
• unsupervised domain adaptation
• class imbalance
• rolling bearing