Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network

Purpose The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing. Design/methodology/approach The vibration signal data of rolling bearing has long time series and strong noise interference, which brings great difficulties for the accurate diagnosis of bearin...

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Veröffentlicht in:	Industrial lubrication and tribology Jg. 72; H. 7; S. 947 - 953
Hauptverfasser:	Che, Changchang, Wang, Huawei, Ni, Xiaomei, Fu, Qiang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Bradford Emerald Publishing Limited 17.09.2020 Emerald Group Publishing Limited
Schlagworte:	Artificial neural networks Back propagation Classification Convolution Fault diagnosis Feature extraction Interference Methods Model accuracy Neural networks Noise Noise levels Noise reduction Propagation Roller bearings Signal processing Time series Vibration Fault diagnosis Stacked denoising autoencoder (SDAE) Convolutional neural network (CNN) Rolling bearing Vibration signal
ISSN:	0036-8792, 1758-5775
Online-Zugang:	Volltext
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