Aug 2026· IEEE Sensors Journal· Vol 26, pp. 25321-25331· 0 citations· 43 references
Abstract
Domain generalization (DG) has been widely applied to rotating machinery fault diagnosis. Under extreme working conditions and coupled influencing factors, existing DG methods are prone to issues such as batch oscillations and early gradient optimization conflicts. To address these issues, this article proposes a novel method named the gradient-driven learnable class-center network (GLC-Net). GLC-Net incorporates two core mechanisms: 1) a globally learnable center for momentum updates. GLC-Net replaces passive batch-wise statistical updates with gradient-driven updates to the center parameters, and combines this with a momentum mechanism to alleviate optimization oscillations and 2) dynamic curriculum learning strategy is introduced. This strategy effectively prevents negative transfer by balancing the learning of discriminative features and domain-invariant features. Experimental results on both bearing and gearbox datasets demonstrate that GLC-Net achieves superior generalization performance and robustness.
A novel joint hierarchical suppression framework 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 and is embedded into fully connected layers to drive the model to learn residual domain...
Tianlong Huo, Rongzhen Zhao, Jun Gong et al.· Structural Health Monitoring· 0 citations
DDDG, a dendrite-network-assisted multi-source domain generalization framework for fault diagnosis in rotating machinery, is proposed, and the results demonstrate that DDDG achieves high accuracy under unseen operating conditions, exhibiting strong generalizability.
Peng-Cheng Liao, Xiaofei Bai, Rong Cheng et al.· Applied Sciences· 0 citations
A multi-source domain generalization framework for bearing fault diagnosis based on class-conditional feature distance weighting and mechanism–style disentanglement that can effectively mitigate the influence of domain-related interference on fault identification and improve diagnostic stability in unseen domains is pr...
Chun-Yang Dai, En-Yong Xu, Yuan Xu et al.· Measurement science and tech...· 0 citations
A novel GCU-SAM enhanced Transformer for intelligent fault diagnosis that exhibits exceptional feature-clustering discriminative capability and convergence stability, providing a robust and highly accurate solution for the intelligent fault diagnosis of complex rotating machinery.
Jing Li, Lei Hu, Peng Luo· Italian National Conference...· 0 citations
A new single-source domain generalization model named adaptive mask flow adversarial network (AMFAN) is proposed, aiming to enhance generalization capability by effective cross-domain simulation based on learnable perturbations in feature space.
Jun Wang, Bo Yu, He Ren et al.· ISA transactions· 0 citations
Rolling bearings are a critical component for the reliability of rotating machinery, but traditional fault diagnosis methods rely on manually designed features, resulting in insufficient robustness under varying operating conditions. To address this issue, this paper proposes an evolutionary optimization framework that...
