Speed-Robust Feature Learning and Minor-Class Generation for Imbalanced Variable-Speed Bearing Fault Diagnosis
提出VIB-FGNet方法，通过变分信息瓶颈学习速度不变特征，并在共享潜空间生成少数类样本，同时解决变速与类别不平衡问题，实验证明优于现有方法。
Variable-speed operation and class imbalance are two prevalent challenges in rolling bearing fault diagnosis; However, existing methods seldom tackle both simultaneously, which limits their diagnostic effectiveness. To address the above issues, this article proposes a variational information bottleneck-based speed-robust feature learning and minor-class feature generation (VIB-FGNet) for imbalanced fault diagnosis under variable-speed conditions. The proposed method extracts speed-invariant fault representations in the latent space through the designed multiple constraints, thereby effectively suppressing condition-related disturbances. On this basis, a class-level feature generation strategy is applied in the shared cross-speed latent space, dynamically augmenting minority-class fault features via reparameterized sampling to effectively alleviate sample imbalance. Experimental results demonstrate that across different variable-speed and sample-ratio settings, the proposed method exhibits significant advantages over state-of-the-art approaches, validating its effectiveness for variable-speed and imbalanced fault diagnosis tasks.