Federated transfer learning for intelligent fault diagnostics using deep adversarial networks with data privacy
Intelligent data-driven machinery fault diagnosis methods have been popularly developed in
the past years. While fairly high diagnosis accuracies have been obtained, large amounts of …
the past years. While fairly high diagnosis accuracies have been obtained, large amounts of …
Federated adversarial domain generalization network: A novel machinery fault diagnosis method with data privacy
Abstract Domain generalization (DG) methods have been successfully proposed to enhance
the generalization ability of the intelligent diagnosis model. However, these methods hardly …
the generalization ability of the intelligent diagnosis model. However, these methods hardly …
Federated transfer learning in fault diagnosis under data privacy with target self-adaptation
X Li, C Zhang, X Li, W Zhang - Journal of Manufacturing Systems, 2023 - Elsevier
The past decades have witnessed great developments and applications of the data-driven
machinery fault diagnosis methods. Due to the difficulties and significant expenses in …
machinery fault diagnosis methods. Due to the difficulties and significant expenses in …
Federated multi-source domain adversarial adaptation framework for machinery fault diagnosis with data privacy
Transfer learning can effectively solve the target task identification problem with the
prerequisite of sharing all user data and target data, and has become one of the most …
prerequisite of sharing all user data and target data, and has become one of the most …
Federated transfer learning for bearing fault diagnosis with discrepancy-based weighted federated averaging
Generally, high performance of deep learning (DL)-based machinery fault diagnosis
methods relies on abundant labeled fault samples under various working conditions, while …
methods relies on abundant labeled fault samples under various working conditions, while …
Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions
Federated learning has been receiving increasing attention in the recent years, which
improves model performance with data privacy among different clients. The intelligent fault …
improves model performance with data privacy among different clients. The intelligent fault …
Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis
W Zhang, Z Wang, X Li - Reliability Engineering & System Safety, 2023 - Elsevier
Due to the limitations of data quality and quantity of a single industrial user, the development
of intelligent machinery fault diagnosis methods has been reaching a bottleneck in the …
of intelligent machinery fault diagnosis methods has been reaching a bottleneck in the …
Residual joint adaptation adversarial network for intelligent transfer fault diagnosis
Although deep networks based diagnostic methods have been increasingly studied and
acquired certain achievements in recent years, most of them suppose that the training and …
acquired certain achievements in recent years, most of them suppose that the training and …
Federated contrastive prototype learning: An efficient collaborative fault diagnosis method with data privacy
Data-driven fault diagnosis approaches have attracted considerable attention in the past few
years, and promising diagnostic performance has been achieved with sufficient monitoring …
years, and promising diagnostic performance has been achieved with sufficient monitoring …
A federated transfer learning method with low-quality knowledge filtering and dynamic model aggregation for rolling bearing fault diagnosis
Intelligent mechanical fault diagnosis techniques have been extensively developed in recent
years. Owing to the advantage of data privacy protection, federated learning has recently …
years. Owing to the advantage of data privacy protection, federated learning has recently …