Intelligent data-driven machinery fault diagnosis methods have been successfully and
popularly developed in the past years. While promising diagnostic performance has been …

Intelligent mechanical fault diagnosis techniques have been extensively developed in recent
years. Owing to the advantage of data privacy protection, federated learning has recently …

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 …

With the coming of the industrial Big Data era, data-driven fault diagnosis models emerge
recently and show potential results in many studies. However, it is impractical to collect …

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 …

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 …

Federated learning has been receiving increasing attention in the recent years, which
improves model performance with data privacy among different clients. The intelligent fault …

The federated learning (FL) method based on model aggregation can balance data and
protect data privacy, but the existing method is difficult to achieve the same effectiveness as …

Generally, high performance of deep learning (DL)-based machinery fault diagnosis
methods relies on abundant labeled fault samples under various working conditions, while …

Privacy protection as a major concern of the industrial big data enabling entities makes the
massive safety-critical operation data of a wind turbine unable to exert its great value …