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...
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| Vydáno v: | Measurement : journal of the International Measurement Confederation Ročník 189; s. 110460 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
Elsevier Ltd
15.02.2022
Elsevier Science Ltd |
| Témata: | |
| ISSN: | 0263-2241, 1873-412X |
| On-line přístup: | Získat plný text |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 110460 |
| Author | Chen, Fei Luo, Wei Yang, Zheng Xu, Binbin |
| Author_xml | – sequence: 1 givenname: Zheng surname: Yang fullname: Yang, Zheng organization: Key Laboratory of CNC Equipment Reliability, Ministry of Education, School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China – sequence: 2 givenname: Binbin surname: Xu fullname: Xu, Binbin organization: Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China – sequence: 3 givenname: Wei surname: Luo fullname: Luo, Wei organization: Key Laboratory of CNC Equipment Reliability, Ministry of Education, School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China – sequence: 4 givenname: Fei surname: Chen fullname: Chen, Fei email: chenfei@sztu.edu.cn organization: Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China |
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