Data synthesis using dual discriminator conditional generative adversarial networks for imbalanced fault diagnosis of rolling bearings-Reference-Cited by-同舟云学术

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Data synthesis using dual discriminator conditional generative adversarial networks for imbalanced fault diagnosis of rolling bearings

Published:2020-07 Issue: Volume:158 Page:107741
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Zheng Taisheng,Song Lei^ORCID,Wang Jianxing,Teng Wei,Xu Xiaoli,Ma Chao^ORCID

Funder

National Natural Science Foundation of China

Foundation of Key Laboratory of Space Utilization, Technology and Engineering Center for Space utilization, Chinese Academy of Sciences

Open Project of Beijing key Laboratory of Measurement and Control of Mechanical and Electrical System

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference37 articles.

1. Fault diagnosis of rolling element bearings using fractional linear prediction and AI techniques;Sharma;Life Cycle Reliab. Saf. Eng.,2019

2. Deep coupling autoencoder for fault diagnosis with multimodal sensory data;Ma;IEEE Trans. Ind. Inf.,2018

3. Nonlinear dynamic investigations on rolling element bearings: A review;Sharma;Adv. Mech. Eng.,2018

4. An enhancement denoising autoencoder for rolling bearing fault diagnosis;Zong;Measurement,2018

5. Novel ensemble techniques for classification of rolling element bearing faults;Sharmal;J. Braz. Soc. Mech. Sci. Eng.,2017

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