Autoencoder-based representation learning and its application in intelligent fault diagnosis: A review

With the increase of the scale and complexity of mechanical equipment, traditional intelligent fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the demand of coupling faults. In the past decades, the vigorous development of deep learning (DL) brings new opportunities...

Full description

Saved in:
Bibliographic Details
Published in:Measurement : journal of the International Measurement Confederation Vol. 189; p. 110460
Main Authors: Yang, Zheng, Xu, Binbin, Luo, Wei, Chen, Fei
Format: Journal Article
Language:English
Published: London Elsevier Ltd 15.02.2022
Elsevier Science Ltd
Subjects:
Application
Autoencoder
Complexity
Deep learning
Fault diagnosis
Intelligent fault diagnosis
Intelligent systems
Machine learning
Mathematical models
Optimization
Representation learning
Representations
Representation learning
Intelligent fault diagnosis
Autoencoder
Application
Optimization
ISSN:0263-2241, 1873-412X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With the increase of the scale and complexity of mechanical equipment, traditional intelligent fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the demand of coupling faults. In the past decades, the vigorous development of deep learning (DL) brings new opportunities for IFD, especially the representation learning based on Autoencoder (AE) theory has been widely applied. To provide a more comprehensive reference, the theoretical foundations of multi-type AEs and the training method of stacked autoencoder (SAE) are briefly introduced. Then the application advances of AE are reviewed from optimization and combination aspects, which are aiming at improving the representation learning ability. To provide ways for the application of AE-based methods, two typical study cases for ideal and complex engineering systems are illustrated respectively. Finally, the challenges and prospects of AE-based representation learning are reported from four aspects, which give a guidance for the future research direction.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2021.110460