In recent years, various deep learning models have been developed for the fault diagnosis
of rotating machines. However, in practical applications related to fault diagnosis, it is difficult …

This paper presents a new, intelligent diagnostic method for identifying structural faults in
rotating machinery based on distinctive frequency components (DFCs) and support vector …

As the concept of Industry 4.0 is introduced, artificial intelligence-based fault analysis is
attracted the corresponding community to develop effective intelligent fault diagnosis and …

In view of the limitations of existing rotating machine fault diagnosis methods in single-scale
signal analysis, a fault diagnosis method based on multi-scale permutation entropy (MPE) …

Feature extraction and class discrimination are two key problems for fault diagnosis of
rotating machinery. Firstly, multi-scale higher order singular spectrum analysis (MS-HO …

Intelligent fault diagnosis methods have been widely developed in recent years due to the
ability in learning diagnosis knowledge from monitoring data automatically. However, for …

The fault detection of rotating machinery systems especially its typical components such as
bearings and gears is of special importance for maintaining machine systems working …

Based on kernel density estimation (KDE) and Kullback-Leibler divergence (KLID), a new
data-driven fault diagnosis method is proposed from a statistical perspective. The ensemble …

In the modern manufacturing industry, mechanical equipment plays a crucial role.
Equipment working in harsh environments for a long time is more likely to break down …

The primary task of rotating machinery fault diagnosis is to extract more fault feature
information from the measured signals, so that its diagnostic result is more accurate and …