Wind turbine gearbox fault diagnosis based on an improved supervised autoencoder using vibration and motor current signals-Reference-Cited by-同舟云学术

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Wind turbine gearbox fault diagnosis based on an improved supervised autoencoder using vibration and motor current signals

Published:2021-07-15 Issue:11 Volume:32 Page:114003
ISSN:0957-0233
Container-title:Measurement Science and Technology
language:
Short-container-title:Meas. Sci. Technol.

Author:

Yang Shuai^ORCID,Wang Yu^ORCID,Li Chuan

Funder

Research Start-Up Funds of Chongqing Technology and Business University

Key Project of the Chongqing Natural Science Foundation

Chongqing Natural Science Foundation

Science, Technology Research Program of Chongqing Municipal Education Commission

National Natural Science Foundation of China

Publisher

IOP Publishing

Subject

Applied Mathematics,Instrumentation,Engineering (miscellaneous)

Link

https://iopscience.iop.org/article/10.1088/1361-6501/ac0741/pdf

Reference35 articles.

1. Review of wind turbine research in 21st century;Amano;J. Energy Resour. Technol.,2017

2. Failure rate and downtime survey of wind turbines located in Spain;Artigao;IET Renew. Power Gener.,2021

3. A review on wind turbines gearbox fault diagnosis methods;Gu;J. Vibroeng.,2021

4. Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network;Shao;IEEE Trans. Ind. Electron.,2017

5. Vibration spectrum imaging: a novel bearing fault classification approach;Amar;IEEE Trans. Ind. Electron.,2014

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