A Fault Diagnosis Method for Rolling Bearings Based on Parameter Transfer Learning under Imbalance Data Sets

A Fault Diagnosis Method for Rolling Bearings Based on Parameter Transfer Learning under Imbalance Data Sets

Fault diagnosis under the condition of data sets or samples with only a few fault labels has become a hot spot in the field of machinery fault diagnosis. To solve this problem, a fault diagnosis method based on deep transfer learning is proposed. Firstly, the discriminator of the generative adversar...

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Bibliographic Details
Main Authors:	Cheng Peng, Lingling Li, Qing Chen, Zhaohui Tang, Weihua Gui, Jing He
Format:	Article
Language:	English
Published:	MDPI AG 2021-02-01
Series:	Energies
Subjects:	fault diagnosis rolling bearings unbalance samples deep transfer learning
Online Access:	https://www.mdpi.com/1996-1073/14/4/944

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