Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings

This paper proposes a new stacked pruning sparse denoising autoencoder (sPSDAE) model for intelligent fault diagnosis of rolling bearings. Different from the traditional autoencoder, the proposed sPSDAE model, including a fully connected autoencoder network, uses the superior features extracted in a...

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Vydané v:	Applied soft computing Ročník 88; s. 106060
Hlavní autori:	Zhu, Haiping, Cheng, Jiaxin, Zhang, Cong, Wu, Jun, Shao, Xinyu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.03.2020
Predmet:	Deep learning Fault diagnosis Pruning operation Rolling bearing Stacked pruning sparse denoising autoencoder Deep learning Fault diagnosis Stacked pruning sparse denoising autoencoder Pruning operation Rolling bearing
ISSN:	1568-4946, 1872-9681
On-line prístup:	Získať plný text
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