A domain feature decoupling network for rotating machinery fault diagnosis under unseen operating conditions

Author:

Gao TianyuORCID,Yang JingliORCID,Wang Wenmin,Fan XiaopengORCID

Funder

China Postdoctoral Science Foundation

Ministry of Industry and Information Technology of the People's Republic of China

Publisher

Elsevier BV

Reference46 articles.

1. Dual weighted-class adversarial network for rotary machine fault diagnosis using multisource domain with class-inconsistent data;Yang;IEEE/ASME Trans Mechatronics,2024

2. Federated adversarial domain generalization network: A novel machinery fault diagnosis method with data privacy;Wang;Knowl-Based Syst,2023

3. A fault location method based on ensemble complex spatio-temporal attention network for complex systems under fluctuating operating condition;Yang;Appl Soft Comput,2023

4. Time-frequency supervised contrastive learning via pseudo-labeling: An unsupervised domain adaptation network for rolling bearing fault diagnosis under time-varying speeds;Pang;Adv Eng Inform,2023

5. Adaptive open set domain generalization network: Learning to diagnose unknown faults under unknown working conditions;Zhao;Reliab Eng Syst Saf,2022

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