The fault diagnosis method of rolling bearing under variable working conditions based on deep transfer learning

The vibration signals of rolling bearing obtained under variable working conditions do not obey the same independent distribution so that the traditional method of bearing fault diagnosis has low accuracy, a fault diagnosis method about rolling bearing based on sparse denoising autoencoder (SDAE) fo...

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Bibliographic Details
Published in:	Journal of the Brazilian Society of Mechanical Sciences and Engineering Vol. 42; no. 11
Main Authors:	Dong, Shaojiang, He, Kun, Tang, Baoping
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2020 Springer Nature B.V
Subjects:	Algorithms Engineering Fault diagnosis Feature extraction Fourier transforms Learning Mechanical Engineering Noise reduction Roller bearings Statistical methods Technical Paper Vibration Working conditions Fault diagnosis Variable working conditions Transfer learning Rolling bearing Sparse denoising autoencoder
ISSN:	1678-5878, 1806-3691
Online Access:	Get full text
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