  Modern industrial fault diagnosis tasks often face the combined challenge of
distribution discrepancy and bi-imbalance. Existing domain adaptation
approaches pay little attention to the prevailing bi-imbalance, leading to poor
domain adaptation performance or even negative transfer. In this work, we
propose a self-degraded contrastive domain adaptation (Sd-CDA) diagnosis
framework to handle the domain discrepancy under the bi-imbalanced data. It
first pre-trains the feature extractor via imbalance-aware contrastive learning
based on model pruning to learn the feature representation efficiently in a
self-supervised manner. Then it forces the samples away from the domain
boundary based on supervised contrastive domain adversarial learning
(SupCon-DA) and ensures the features generated by the feature extractor are
discriminative enough. Furthermore, we propose the pruned contrastive domain
adversarial learning (PSupCon-DA) to pay automatically re-weighted attention to
the minorities to enhance the performance towards bi-imbalanced data. We show
the superiority of the proposed method via two experiments.
