An attention guided joint distribution domain adaptation method for fault diagnosis

Abstract

Deep domain adaptation (DA) methods are widely employed to address data distribution inconsistencies in engineering scenarios. Although DA methods have demonstrated effectiveness in cross-domain fault diagnosis, several limitations remain. Most studies primarily focus on aligning marginal distributions while often overlooking the alignment of conditional distributions, which can lead to inaccurate class alignment. To enhance the alignment between source and target domains and reduce class confusion at the decision boundary, an attention-guided joint distribution domain adaptation method (DRCDA) is proposed. First, domain adversarial neural networks (DANN) are utilized to align marginal distributions between the source and target domains. Next, a class alignment module is introduced to align conditional distributions by computing inter-class distances and applying multi-kernel maximum mean discrepancy (MK-MMD). This method incorporates pseudo-labels from the target domain alongside true labels from the source domain, facilitating compact clustering of similar features while ensuring separation between different classes. Additionally, an adaptive threshold pseudo-labeling strategy is designed to address the issue of low-quality pseudo-labels in the target domain. To further mitigate negative transfer effects, an inter-domain attention module is proposed to explore transferable contextual information and model the correlation between the source and target domains. The effectiveness of the DRCDA method is evaluated using two bearing datasets and one gearbox dataset. Experimental results confirm the superiority of the proposed method in cross-condition diagnostic tasks.