In this survey paper, we systematically summarize existing literature on bearing fault
diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) …

With the rapid development of fault prognostics and health management (PHM) technology,
more and more deep learning algorithms have been applied to the intelligent fault diagnosis …

This study proposes an efficient rolling bearing fault diagnosis model of a hybrid neural
network with a lightweight attention mechanism. Firstly, to achieve the low complexity of …

In this paper, a new strategy based on the fusion of different Support Vector Machines (SVM)
is proposed in order to reduce noise effect in bearing fault diagnosis systems. Each SVM …

This paper provides an overview of several condition monitoring (CM) techniques for the
alternating current (AC) motor in a precise manner and this will be useful when selecting …

Adjustable speed drives (ASDs) are widely used in industry for controlling electric motors in
applications such as rolling mills, compressors, fans, and pumps. Condition monitoring of …

In this paper an intelligent condition monitoring of induction motor based on the wavelet
packet decomposition and time domain features have been presented. The classification …

A novel intelligent diagnostic condition monitoring system for electric motors via
instantaneous power analysis is proposed. Although several types of condition monitoring …

This work presents machine learning-based bearing defect detection of three-phase
induction motor fed by variable frequency drive. Multi band-pass filters, fast Fourier transform …

Induction motors are the most popular motors in the world. Unscheduled breakdowns often
lead to financial losses. The most common failure of induction motors is bearing related …