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.

通过自我降级的对比域自适应 (Sd-CDA) 诊断框架来处理不平衡数据下的域差异问题，首先通过基于模型剪枝的不平衡感知对比学习方法对特征提取器进行预训练，以自监督方式高效地学习特征表示；然后基于监督对比域对抗学习 (SupCon-DA) 强制将样本远离域边界，确保特征生成器生成的特征具有足够的区分性；此外，提出了剪枝对比域对抗学习 (PSupCon-DA)，自动关注少数类以增强对不平衡数据的性能；通过两个实验展示了所提方法的优越性。