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

Abstract Prognostics and Health Management (PHM), including monitoring, diagnosis,
prognosis, and health management, occupies an increasingly important position in reducing …

The distribution of the health data monitored from mechanical system in the industries is
class imbalanced mainly. The amount of monitoring data for the normal condition is far more …

Fault diagnosis and prognosis are some of the most crucial functionalities in complex and
safety-critical engineering systems, and particularly fault diagnosis, has been a subject of …

In this paper, a practical machine learning-based fault diagnosis method is proposed for
induction motors using experimental data. Various single-and multi-electrical and/or …

Bearing fault diagnosis suffers from class imbalances and distributional discrepancies of
fault data under different working conditions. The class imbalance of the fault class …

Cyber-physical-social system (CPSS) has drawn tremendous attention in industrial
applications such as industrial Internet of Things (IIoT). As the fundamental component of …

The fault characteristics of the rolling bearings of wind turbine gearboxes are unstable under
actual operating conditions. Problems such as inadequate fault sample data, imbalanced …

An imbalanced number of faulty and normal samples causes serious damage to the
performance of the conventional diagnosis methods. To settle the data-imbalance fault …

Intelligent fault diagnosis based on deep neural networks and big data has been an
attractive field and shows great prospects for applications. However, applications in practice …