Zhao-Dong Liu

A bearing fault diagnosis method based on MCDCGAN and DPTAN under data imbalance

A diagnostic framework that combines generative-adversarial data augmentation with dual-branch time–frequency representation learning to improve feature quality and fault classification and achieves better performance than baseline models and maintains robustness under data imbalance and noisy conditions is proposed.