Source domain selection method for domain generalization bearing fault diagnosis model

Abstract Domain generalization methods for bearing fault diagnosis aim to enhance model generalization under unseen working conditions. However, distribution discrepancies between source and target domains often degrade diagnostic accuracy. While current research predominantly focuses on model optimization, the critical role of source domain composition remains underexplored. To address this gap, this paper introduces MMD-VD—a novel dual-criterion source domain selection method based on Maximum Mean Discrepancy (MMD) and Variance Discrepancy (VD). The proposed approach innovatively repurposes these metrics from traditional roles as loss functions to a proactive selection mechanism, dynamically optimizing both the quantity and combination of source domains to significantly improve cross-condition generalization. Extensive validation on publicly available CWRU and HUST bearing datasets demonstrates the effectiveness of the proposed method. The optimal source domain combinations selected by MMD-VD achieved diagnostic accuracies remarkably close to the empirically optimal combinations, with deviations of only 0.14% on the CWRU dataset and 0.64% on the HUST dataset under cross-condition scenarios.