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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Bibliographic Details
Published in:	Industrial lubrication and tribology Vol. 72; no. 7; pp. 947 - 953
Main Authors:	Che, Changchang, Wang, Huawei, Ni, Xiaomei, Fu, Qiang
Format:	Journal Article
Language:	English
Published:	Bradford Emerald Publishing Limited 17.09.2020 Emerald Group Publishing Limited
Subjects:	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 Access:	Get full text
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