Self-degraded contrastive domain adaptation for industrial fault diagnosis with bi-imbalanced data

• URL: http://arxiv.org/abs/2405.20700v1
• Date: Fri, 31 May 2024 08:51:57 GMT
• Title: Self-degraded contrastive domain adaptation for industrial fault diagnosis with bi-imbalanced data
• Authors: Gecheng Chen, Zeyu Yang, Chengwen Luo, Jianqiang Li,
• Abstract summary: We propose a self-degraded contrastive domain adaptation 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. Then it forces the samples away from the domain boundary based on supervised contrastive domain adversarial learning.
• Score: 7.6544734853901035
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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