A generalization bearing fault diagnosis method via joint hierarchical suppression of domain features

Domain generalization (DG)-based fault diagnosis can effectively suppress domain drift interference induced by varying operating conditions of rolling bearings, enabling the model to maintain high accuracy and stable diagnostic performance under unseen working scenarios. However, most existing DG methods passively extract domain-invariant features via domain alignment, feature decoupling or data augmentation. Due to the absence of explicit constraints, their performance is restricted and unstable. Furthermore, they fail to take into account the suppression of spurious features triggering “shortcut learning,” which further degrades the generalization capability of the model. To address these limitations, this paper proposes a novel joint hierarchical suppression framework. Firstly, a cross-dimensional suppression algorithm is developed, which collaboratively operates on channel and spatial dimensions under domain label supervision to identify and remove domain-specific features in shallow network layers. Secondly, a global feature suppression mechanism is embedded into fully connected layers to drive the model to learn residual domain-invariant features, thus greatly boosting generalization performance. Finally, the above modules are integrated into an end-to-end trainable architecture to systematically extract robust domain-invariant representations. The proposed approach is verified on three rolling bearing datasets from the University of Ottawa, Huazhong University of Science and Technology, and Lanzhou University of Technology under unseen working conditions, achieving average diagnostic accuracies of 98.73, 96.32, and 99.22%, respectively. Experimental results verify the effectiveness and superiority of the presented method in identifying and eliminating domain-specific features.