Imbalanced domain generalization via Semantic-Discriminative augmentation for intelligent fault diagnosis

Domain generalization-based fault diagnosis (DGFD) has garnered significant attention due to its ability to generalize prior diagnostic knowledge to unseen working conditions or machines. However, existing DGFD methods are generally implemented under the premise of class balance, which may not accurately reflect real -world diagnosis scenarios since fault data collected in practical engineering often exhibits severe class imbalance. To address this challenge, this paper proposes a semantic-discriminative augmentation-driven network for imbalanced domain generalization fault diagnosis. A semantic regularization-based mixup strategy is devised to synthesize sufficient reliable samples to compensate for minority classes. Subsequently, discriminative representations are acquired by minimizing the triplet loss, thereby enhancing the model's generalization capabilities. Extensive evaluations, including cross-working condition and cross-machine tasks, demonstrate the effectiveness and superiority of the proposed method.

Automated summary

This paper proposes a semantic-discriminative augmentation-driven network for imbalanced domain generalization fault diagnosis, which enhances the model's generalization capabilities through synthesizing reliable samples and optimizing representations.