The following publications are possibly variants of this publication:
- A Hybrid Adversarial Domain Adaptation Network for Bearing Fault Diagnosis Under Varying Working ConditionsZiyun Zhang, Lei Peng 0001, Guangming Dai, Maocai Wang, Junfei Bai, Lei Zhang, Jian Li. tim, 72:1-13, 2023. [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]
- A Transfer Learning Framework with a One-Dimensional Deep Subdomain Adaptation Network for Bearing Fault Diagnosis under Different Working ConditionsRuixin Zhang, Yu Gu. sensors, 22(4):1624, 2022. [doi]
- A study on adaptation lightweight architecture based deep learning models for bearing fault diagnosis under varying working conditionsJie Wu, Tang Tang, Ming Chen, Yi Wang, Kesheng Wang. eswa, 160:113710, 2020. [doi]
- Class Subdomain Adaptation Network for Bearing Fault Diagnosis Under Variable Working ConditionsLu Zhang, Hua Li, Jie Cui, Wei Li, Xiaodong Wang. tim, 72:1-17, 2023. [doi]
- Bearing Fault Diagnosis under Variable Working Conditions Based on Deep Residual Shrinkage Networks and Transfer LearningXinyu Yang, Fulin Chi, Siyu Shao, Qiang Zhang. js, 2021:1-13, 2021. [doi]
- A Deep Transfer Model With Wasserstein Distance Guided Multi-Adversarial Networks for Bearing Fault Diagnosis Under Different Working ConditionsMing Zhang, Duo Wang, Weining Lu, Jun Yang, Zhiheng Li, Bin Liang. access, 7:65303-65318, 2019. [doi]