As the crucial part of the health management and condition monitoring of mechanical
equipment, the fault diagnosis and pattern recognition using vibration signal are essential …

In the absence of a priori knowledge, manual feature selection is too blind to find the
sensitive features which can effectively classify the different fault features. And it is difficult to …

Traditional labour‐intensive methods always suffer from time and frequency blurring and
cross‐term interference when detecting fault features from vibration signals and their time …

Fault diagnosis is important for the maintenance of rotating machinery. The detection of
faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this …

Because multi-fault vibration signals in rotating machinery are often more complicated than
single faults, human-designed fault feature sets are not yet able to respond adequately to …

Rolling element bearings and gears are the most common machine elements. As they are
extensively used in rotating machinery, their health conditions are crucial to the safe …

Intelligent fault diagnosis methods of rotating machinery have attracted much attention in
recent years. In this paper, an intelligent deep learning based method named deep recurrent …

Fault diagnosis of rotating machinery plays a significant role in the reliability and safety of
modern industrial systems. The traditional fault diagnosis methods usually need manually …

Fault diagnosis of rotating machinery is essential for maintaining system performance and
ensuring the operation safety. Deep learning (DL) has been recently developed rapidly and …

Traditional intelligent fault diagnosis methods focus on distinguishing different fault modes,
but ignore the deterioration of fault severity. This paper proposes a new two-stage …