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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Vydáno v:	Mechanical systems and signal processing Ročník 149; s. 107327
Hlavní autoři:	Wu, Xinya, Zhang, Yan, Cheng, Changming, Peng, Zhike
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin Elsevier Ltd 15.02.2021 Elsevier BV
Témata:	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
On-line přístup:	Získat plný text
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