Deep transfer learning for rolling bearing fault diagnosis under variable operating conditions

Deep transfer learning for rolling bearing fault diagnosis under variable operating conditions

Rolling bearings are the vital components of rotary machines. The collected data of rolling bearing have strong noise interference, massive unlabeled samples, and different fault features. Thus, a deep transfer learning method is proposed for rolling bearings fault diagnosis under variable operating...

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Bibliographic Details
Main Authors:	Changchang Che, Huawei Wang, Qiang Fu, Xiaomei Ni
Format:	Article
Language:	English
Published:	SAGE Publishing 2019-12-01
Series:	Advances in Mechanical Engineering
Online Access:	https://doi.org/10.1177/1687814019897212

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