Cloud-edge collaborative transfer fault diagnosis of rotating machinery via federated fine-tuning and target self-adaptation

Author:

Wang Rui,Huang WeiguoORCID,Lu Yixiang,Wang Jun,Ding ChuancangORCID,Liao Yi,Shi Juanjuan

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference35 articles.

1. Domain adaptation network base on contrastive learning for bearings fault diagnosis under variable working conditions;An;Expert Systems with Applications,2023

2. Brendan McMahan, H., Moore, E., Ramage, D., Hampson, S., & Aguera y Arcas, B. (2017). Communication-efficient learning of deep networks from decentralized data. In 20th International Conference on Artificial Intelligence and Statistics.

3. Federated transfer learning for bearing fault diagnosis with discrepancy-based weighted federated averaging;Chen;IEEE Transactions on Instrumentation and Measurement,2022

4. A multi-source weighted deep transfer network for open-set fault diagnosis of rotary machinery;Chen;IEEE Transactions on Cybernetics,2023

5. Industrial edge intelligence: Federated-meta learning framework for few-shot fault diagnosis;Chen;IEEE Transactions on Network Science and Engineering,2023

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