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

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

In the past year, various deep learning-based fault diagnosis methods have been designed
to guarantee the stable, safe, and efficient operation of electromechanical systems. To …

In fault diagnosis field, inconsistent distribution between training and testing data, resulted
from variable working conditions of rotating machinery, inevitably leads to degradation of …

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 research of intelligent fault diagnosis method has made great progress. The
prerequisite for the effectiveness of most intelligent diagnosis models is to have abundant …

Rolling bearing fault diagnosis is closely related to the safety of mechanical system. In real-
world diagnosis, it is difficult to obtain abundant labeled data due to varying operation …

On account of lacking labeled samples for the bearing fault diagnosis in real engineering
applications, transfer learning is widely investigated for transferring diagnosis information. A …

With the accumulation of data, the intelligent fault diagnosis of rolling bearings has achieved
fruitful results, but it is costly to acquire and label data for industrial application. A series of …

Rolling bearing fault diagnosis with unlabeled data is a meaningful yet challenging task.
Recently, deep transfer learning methods with maximum mean discrepancy (MMD) have …