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.

翻译：现代工业故障诊断任务常面临分布差异与双不平衡的双重挑战。现有域适应方法对普遍存在的双不平衡问题关注不足，导致域适应性能不佳甚至产生负迁移。本文提出一种自退化对比域适应诊断框架，以处理双不平衡数据下的域差异问题。该框架首先通过基于模型剪枝的不平衡感知对比学习预训练特征提取器，以自监督方式高效学习特征表示；随后基于监督对比域对抗学习，迫使样本远离域边界，并确保特征提取器生成的特征具有足够判别性。进一步提出剪枝对比域对抗学习方法，通过自动重加权机制关注少数类样本，从而提升双不平衡数据下的诊断性能。两组实验验证了所提方法的优越性。