Aug 2026· International Journal of Data Science and Analysis· Vol 22· 0 citations· 41 references
TL;DR
Extensive experiments on multiple bearing fault datasets demonstrate that CSR-DGAN outperforms existing generative augmentation methods in terms of distribution similarity, cross-domain consistency, and downstream diagnostic performance, highlighting the effectiveness of the proposed problem-driven dual-domain generative framework for robust fault diagnosis under imbalanced conditions.
The experimental results fully demonstrate that the proposed HDCAN can effectively alleviate the distribution shift problem of cross-machine fault data and possesses great application potential in industrial practical scenarios.
Haoran Liu, De-Long Cui, Zhiping Peng et al.· Structural Health Monitoring· 0 citations
An attention-guided joint distribution domain adaptation method (DRCDA) is proposed that incorporates pseudo-labels from the target domain alongside true labels from the source domain, facilitating compact clustering of similar features while ensuring separation between different classes.
Songli Tang, Jianchun Lin· Journal of Vibration and Con...· 0 citations
These results establish PadéNet-enhanced UDA as an accurate, broadly applicable approach for robust bearing fault diagnosis under varying operating conditions, with a reduced parameter count suited to resource-constrained embedded platforms.
Sertac Kilickaya, Cansu Celebioglu, Murat Askar et al.· Machines· 0 citations
A Dynamic Pseudo-Label Guided Adversarial Multi-Scale Graph Convolutional Network (DPAMGCN) for unsupervised cross-domain fault diagnosis and a dynamic threshold-based pseudo-label filtering strategy is proposed that enhances the model's generalization capability.
Jinqi Gao, Bo Zhang, Tianlong Huo et al.· Review of Scientific Instrum...· 0 citations
Results indicate that DPR-PLO provides a feasible and robust solution for privacy-preserving bearing health management in new wind turbines, and adopts a two-stage pseudo-label refinement procedure to reduce noise propagation during self-training.
Haoyang Mao, Yiming Zhang, Weizheng Zhao et al.· Measurement science and tech...· 0 citations
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