With the rapid development of industry, fault diagnosis plays a more and more important role
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …

The traditional deep learning-based bearing fault diagnosis approaches assume that the
training and test data follow the same distribution. This assumption, however, is not always …

Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to
machine fault diagnosis. This is a promising way to release the contribution from human …

Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …

The scope of data-driven fault diagnosis models is greatly extended through deep learning
(DL). However, the classical convolution and recurrent structure have their defects in …

Recently, intelligent fault diagnosis has made great achievements, which has aroused
growing interests in the field of bearing fault diagnosis due to its strong feature learning …

In this paper, we present a deep autoencoder based energy method (DAEM) for the
bending, vibration and buckling analysis of Kirchhoff plates. The DAEM exploits the higher …

The advances of intelligent fault diagnosis in recent years show that deep learning has
strong capability of automatic feature extraction and accurate identification for fault signals …

Abstract Fault Detection and Diagnosis (FDD) of rotating machinery plays a key role in
reducing the maintenance costs of the manufacturing systems. How to improve the FDD …

This paper provides a comprehensive literature review on Predictive Maintenance (PdM)
with emphasis on system architectures, purposes and approaches. In industry, any outages …