The following publications are possibly variants of this publication:
- A collaborative central domain adaptation approach with multi-order graph embedding for bearing fault diagnosis under few-shot samplesWengang Ma, Ruiqi Liu, Jin Guo, ZiCheng Wang, Liang Ma. asc, 140:110243, June 2023. [doi]
- Bearing Fault Diagnosis Under Variable Working Conditions Base on Contrastive Domain Adaptation MethodYiyao An, Ke Zhang 0006, Yi Chai, Qie Liu, Xinghua Huang. tim, 71:1-11, 2022. [doi]
- Bearing Fault Diagnosis Under Variable Working Conditions Based on Domain Adaptation Using Feature Transfer LearningZhe Tong, Wei Li 0019, Bo Zhang, Fan Jiang 0006, Gongbo Zhou. access, 6:76187-76197, 2018. [doi]
- Unsupervised Domain Adaptation for Bearing Fault Diagnosis Considering the Decision BoundariesTianyu Han, Xi Shi, Gang Zhang, Chao Liu. icphm 2021: 1-7 [doi]
- Time-frequency supervised contrastive learning via pseudo-labeling: An unsupervised domain adaptation network for rolling bearing fault diagnosis under time-varying speedsBin Pang, Qiuhai Liu, Zhenduo Sun, Zhenli Xu, Ziyang Hao. aei, 59:102304, 2024. [doi]
- A novel geodesic flow kernel based domain adaptation approach for intelligent fault diagnosis under varying working conditionZhongwei Zhang, Huaihai Chen, Shunming Li, Zenghui An, Jinrui Wang. ijon, 376:54-64, 2020. [doi]
- Gaussian Mixture Variational-Based Transformer Domain Adaptation Fault Diagnosis Method and Its Application in Bearing Fault DiagnosisYiyao An, Ke Zhang 0006, Yi Chai, Zhiqin Zhu, Qie Liu. tii, 20(1):615-625, January 2024. [doi]