Class-Aware Multi-Source Domain Adaptation for Imbalanced Fault Diagnosis

Unsupervised Domain Adaptation (UDA) has been widely used for fault diagnosis to solve data distribution shifts in multi-source domains. However, UDA ignores the imbalance of the class distribution in the domain, which makes it difficult to apply to real industrial processes. Therefore, we propose a class-aware multi-source domain adaptation (CMDA) for imbalanced fault diagnosis. Firstly, a batch interaction feature extractor is designed to apply the cross-attention mechanism to the batch dimension to capture nonlinear relationships among imbalanced features of classes. Secondly, we construct a class-aware UDA module that is introduced with class information in both the feature and discriminant layers to realize domain adaptation. The module performs multilinear augmentation of imbalanced class samples for data alignment. Finally, a task classifier is designed that combines multiple classifiers with reliability-weighted scores to weaken negative transfer and jointly accomplish fault diagnosis of rolling bearings. The reliability-weighted scores are determined by the inter-domain distance and diagnostic accuracy. We conducted experiments using the CWRU dataset under 4 protocols and 12 scenarios and verified the effectiveness of CMDA.