Secure and Private Fault Detections of Wind Turbine Through Blockchain-Enabled Federated Learning-Reference-Cited by-同舟云学术

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Secure and Private Fault Detections of Wind Turbine Through Blockchain-Enabled Federated Learning

Published:2023-10-12 Issue: Volume:19 Page:1-6
ISSN:
Container-title:2023 Global Reliability and Prognostics and Health Management Conference (PHM-Hangzhou)
language:
Short-container-title:

Author:

Guo Xinhao¹,Xiang Yangxia²,Long Zhuo³,Ma Xiaoguang⁴,Jia Runda¹,Jia Jie²

Affiliation:

1. School of Information Science and Engineering, Northeast University,Shenyang,China

2. School of Computer Science and Engineering, Northeast University,Shenyang,China

3. School of Electrical and Information Engineering, Changsha University of Science and Technology,Changsha,China

4. Foshan Graduate School of Innovation, Northeastern University,Foshan,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10482420/10482375/10482486.pdf?arnumber=10482486

Reference19 articles.

1. Theoretical and experimental study of wind turbine drivetrain fault diagnosis by using torsional vibrations and modal estimation

2. Nonlinear dynamic analysis of high-speed gear pair with wear fault and tooth contact temperature for a wind turbine gearbox

3. STOCHASTIC PROGNOSTICS FOR ROLLING ELEMENT BEARINGS

4. A survey of health monitoring systems for wind turbines

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