A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery

•A novel semi-supervised fault diagnosis method is proposed.•The model can be trained using both labeled and unlabeled data simultaneously.•The performance of the proposed method is experimentally validated on two kinds of facilities. Accurate fault diagnosis is critical to the safe and reliable ope...

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Bibliographic Details

Published in:	Mechanical systems and signal processing Vol. 149; p. 107327
Main Authors:	Wu, Xinya, Zhang, Yan, Cheng, Changming, Peng, Zhike
Format:	Journal Article
Language:	English
Published:	Berlin Elsevier Ltd 15.02.2021 Elsevier BV
Subjects:	Classification Datasets Fault diagnosis Feature extraction Hybrid classification autoencoder Hydraulic turbines Machine learning Rotating machinery Semi-supervised learning Fault diagnosis Rotating machinery Semi-supervised learning Hybrid classification autoencoder
ISSN:	0888-3270, 1096-1216
Online Access:	Get full text
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