Deep conditional adversarial subdomain adaptation network for unsupervised mechanical fault diagnosis
Author:
Funder
National Natural Science Foundation of China
Publisher
Elsevier BV
Reference60 articles.
1. Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector machines;Islam;Reliab. Eng. Syst. Saf.,2019
2. Fault transfer diagnosis of rolling bearings across multiple working conditions via subdomain adaptation and improved vision transformer network;Liang;Adv. Eng. Inform.,2023
3. A review of vibration-based gear wear monitoring and prediction techniques;Feng;Mech. Syst. Signal Process.,2023
4. Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning;Xia;Reliab. Eng. Syst. Saf.,2021
5. An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data;Lei;IEEE Trans. Ind. Electron.,2016
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