A novel deep multi-source domain adaptation framework for bearing fault diagnosis based on feature-level and task-specific distribution alignment

In recent years, deep learning has been extensively applied for intelligent fault diagnosis systems. Most of the developed algorithms ignore the domain shift problem and assume distribution of the training data, known as the source domain, is similar to that of testing data, denoted as the target domain. However, in real-world applications, this assumption is not necessarily true. In current work, a novel multi-source domain adaptation deep learning framework for fault diagnosis of rotary machinery is proposed, which aligns the domains in both feature-level and task-level. The proposed Feature-level and Task-specific Distribution alignment multi-source domain adaptation (FTD-MSDA) framework transfers the knowledge from multiple labeled source domains into a single unlabeled target domain by reducing the feature distribution discrepancy between the target domain and each source domain. Sliced Wasserstein discrepancy is utilized to shape task-specific decision boundaries. Besides, the model can be easily reduced to a single-source domain adaptation problem. Also, the model can be readily updated to unsupervised domain adaptation problems in other fields such as image classification and image segmentation. The experimental verification results show the superiority of the proposed framework over state-of-the-art multi-source domain-adaptation models.

Automated summary

The paper proposes a novel multi-source domain adaptation deep learning framework for fault diagnosis of rotary machinery, aligning domains in both feature-level and task-level to address the domain shift problem.