Adaptive inter-intradomain alignment network with class-aware sampling strategy for rolling bearing fault diagnosis

Existing unsupervised domain adaptation approaches primarily focus on reducing the data distribution gap between the source and target domains, often neglecting the influence of class information, leading to inaccurate alignment outcomes. Guided by this observation, this paper proposes an adaptive inter-intradomain discrepancy method to quantify the intra-class and inter-class discrepancies between the source and target domains. Furthermore, an adaptive factor is introduced to dynamically assess their relative importance. Building upon the proposed adaptive inter-intradomain discrepancy approach, we develop an inter-intra-domain alignment network with a class-aware sampling strategy (IDAN-CSS) to distill the feature representations. The class-aware sampling strategy, integrated within IDAN-CSS, facilitates more efficient training. Through multiple transfer diagnosis cases, we comprehensively demonstrate the feasibility and effectiveness of the proposed IDAN-CSS model.