A dual-view alignment-based domain adaptation network for fault diagnosis

Domain adaptation is a major area of interest in intelligent equipment maintenance and fault diagnosis in recent years. Traditional machine/deep-learning-based fault diagnosis methods assume that the source and target domains share the same distribution, which may fail and lead to catastrophic damages. Many domain adaptation-based fault diagnosis methods have been proposed to address the domain shift problem. However, most of them only align global domain distributions and ignore class relationships between domains, which leads to a decline in diagnostic performance. To overcome this deficiency, a dual-view alignment-based domain adaptation network (DVADAN) for fault diagnosis is proposed in this paper. Specifically, the proposed dual-view alignment, consisting of a global (marginal) alignment constructed with maximum mean discrepancy and a local (conditional) alignment calculating the class-centers by Wasserstein distance, is developed to reduce domain distribution discrepancy. Extensive experiments on two test rigs validated the effectiveness of the proposed DVADAN and showed its superiority over state-of-art fault diagnosis methods.

Automated summary

Domain adaptation is crucial in intelligent equipment maintenance and fault diagnosis. Traditional methods often fail due to distribution discrepancies, leading to catastrophic damages. The proposed DVADAN aligns global and local domains effectively, outperforming existing fault diagnosis methods in experimental tests.