The traditional rolling bearing fault diagnosis methods are difficult to achieve effective cross-
domain fault diagnosis. Therefore, a novel deep convolution multi-adversarial domain …

Heart failure is considered one of the leading cause of death around the world. The
diagnosis of heart failure is a challenging task especially in under-developed and …

In a wind turbine, the rolling bearing is the critical component. However, it has a high failure
rate. Therefore, the failure analysis and fault diagnosis of wind power rolling bearings are …

Fault feature extraction and broadband noise elimination are the keys to weak bearing fault
diagnosis. Morphological filtering is a typical fault feature extraction method. However, the …

An auto-restoration tool to minimize the impact of faults is one of the critical requirements in
a power distribution system. A fault-monitoring system is needed for practical remote …

Fault detection and diagnosis of its severity for machine health monitoring can be stated as a
nested classification problem. For a faulty bearing, each fault location whether belonging to …

The detection of rolling-element bearing fault can be accomplished by monitoring and
interpreting a variety of signals, including the vibration, the acoustic noise, and the stator …

In aerospace, marine, and other heavy industries, bearing fault diagnosis has been an
essential part of improving machine life, reducing economic losses, and avoiding safety …

Traditional sample entropy algorithms are limited in their inability to analyze two-
dimensional (2D) time series. Here, we describe a new feature algorithm for 2D time-series …

For a rolling bearing fault that has nonlinearity and nonstationary characteristics, it is difficult
to identify the fault category. A rolling bearing clustering fault diagnosis method based on …