Sharpness-aware debiased alignment for imbalanced domain generalization fault diagnosis

Machinery usually operates under varying conditions, which necessitate fault diagnosis to manage unpredictable conditions. By transferring knowledge from multiple source domains to unseen target domains, domain generalization-based fault diagnosis offers viable solutions. However, it assumes ample training samples but largely overlooks data imbalance in source domains. Moreover, recent advances advocate prioritizing model discriminability over generalization, but lack theoretical guidance. To address these issues, the sharpness-aware debiased alignment method is proposed to ensure diagnosis robustness for imbalanced domain generalization-based fault diagnosis. Its core idea is to reduce generalization errors while ensuring model discriminability simultaneously in a dual-network framework. Specifically, to maintain model discriminability in imbalanced source domains, the self-supervised feature learning is introduced in a student network by enriching data perspectives with feature augmentations and examining intrinsic relationships in a self-supervised manner. On the other hand, to enhance generalization robustness, the debiased sharpness-aware minimization is then developed in the teacher network by borrowing external knowledge to calibrate adversarial parameter perturbations within a debiased loss landscape. Furthermore, to reduce generalization errors to target domains, the fairness-aware gradient alignment is proposed by incorporating fairness attributes to contrastively align divergent gradients derived from source domains within the debiased loss landscape. Finally, a dual-network framework seamlessly integrates sub-networks through a recursive training procedure. Comprehensive experiments conducted on machinery fault diagnosis, chemical process fault diagnosis, and surface defect recognition demonstrate its effectiveness. By comparison with state-of-the-art methods, the proposed method showcases its superiority in achieving generalization robustness under diverse domain differences and data imbalance.