Disentanglement Learning With Adaptive Centroid Alignment for Multiple Target Domains Fault Diagnosis

Most current domain adaptation methods for fault diagnosis focus on single target domain. However, test data often comes from multiple target domains, as machines work under different operating conditions, subsequently generating a more complex and extensive distribution of target data. Unfortunately, single target domain adaptation methods are not adaptive for multiple target domains adaptation (MTDA), which results in transfer performance degradation. To this end, a novel disentanglement learning with adaptive centroid alignment is proposed for MTDA. Specifically for disentanglement learning, two encoders and two classifiers are constructed independently for fault-related and domain-related feature extractions and classifications. Followed by the dual-adversarial strategy, only fault-related but domain-irrelevant features are extracted. Furthermore, to achieve the category alignment, we also propose an adaptive centroid alignment strategy, so that the feature centroids of the same fault category in different domains are enforced to be close to each other. Extensive experiments demonstrate the superiority of our proposed method compared with other popular approaches.