Bearing fault diagnosis under varying operating conditions is crucial for ensuring the reliability of rotating machinery. However, the task is hindered by the unavailability of target-domain data during training, the limited diversity of single-source-domain data, and severe class imbalance. These issues substantially degrade the generalization capability of diagnostic models, and existing single-domain generalization approaches largely overlook the influence of class imbalance. To address these challenges, we propose a novel framework termed order spectrum correction-based imbalanced single-domain generalization (OISDG). OISDG comprises three key components. First, a condition-aware spectral correction module generates domain-invariant order spectral representations by suppressing condition-induced distortions. Second, an uncertainty-aware intra-class mixup strategy enriches minority-class representations by synthesizing informative same-class samples. Third, an uncertainty-aware contrastive module adaptively adjusts anchor weights and temperatures based on normalized uncertainty to enhance intra-class compactness and inter-class separability. Experiments on three benchmark bearing datasets demonstrate that OISDG achieves over 95% accuracy and 91% F-score, outperforming state-of-the-art methods. These results verify that OISDG provides a robust and generalizable solution for fault diagnosis under varying operating conditions.
Jian et al. (Fri,) studied this question.