Roller bearing fault diagnosis using stacked denoising autoencoder in deep learning and Gath–Geva clustering algorithm without principal component analysis and data label

Most deep learning models such as stacked autoencoder (SAE) and stacked denoising autoencoder (SDAE) are used for fault diagnosis with a data label. These models are applied to extract the useful features with several hidden layers, then a classifier is used to complete the fault diagnosis. However,...

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Veröffentlicht in:	Applied soft computing Jg. 73; S. 898 - 913
Hauptverfasser:	Xu, Fan, Tse, Wai tai Peter, Tse, Yiu Lun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.12.2018
Schlagworte:	Deep learning Gath–Geva clustering algorithm Roller bearing fault diagnosis Stacked denoising autoencoder Deep learning Roller bearing fault diagnosis Gath–Geva clustering algorithm Stacked denoising autoencoder
ISSN:	1568-4946, 1872-9681
Online-Zugang:	Volltext
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