Wind Power Fault Detection Based on Vertical Federated Learning-Reference-Cited by-同舟云学术

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Wind Power Fault Detection Based on Vertical Federated Learning

Published:2023-10-12 Issue: Volume:37 Page:1-7
ISSN:
Container-title:2023 Global Reliability and Prognostics and Health Management Conference (PHM-Hangzhou)
language:
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Author:

Yang Dike¹,Long Zhuo²,Ma Xiaoguang³^ORCID,Xu Bin¹

Affiliation:

1. College of Computer Science and Engineering, Northeastern University,Shenyang,China

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

3. College of Information and Engineering, Northeastern University,Shenyang,China

Funder

United States Department of Energy

National Renewable Energy Laboratory

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10482420/10482375/10482444.pdf?arnumber=10482444

Reference19 articles.

1. The Multiclass Fault Diagnosis of Wind Turbine Bearing Based on Multisource Signal Fusion and Deep Learning Generative Model

2. Autocorrelation-based time synchronous averaging for condition monitoring of planetary gearboxes in wind turbines

3. Vibration-based bearing fault detection for operations and maintenance cost reduction in wind energy

4. Fault diagnosis method for rolling bearings based on short-time fourier transform and convolution neural network;Li;J. Vib. Shock,2018

5. Fault diagnosis of roller bearing based on the pseudo wigner-ville distribution and wavelet transform;Ye;Mach. Des. Res.,2012

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