Wind Turbine Blade Breakage Monitoring With Deep Autoencoders-Reference-Cited by-同舟云学术

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Wind Turbine Blade Breakage Monitoring With Deep Autoencoders

Published:2018-07 Issue:4 Volume:9 Page:2824-2833
ISSN:1949-3053
Container-title:IEEE Transactions on Smart Grid
language:
Short-container-title:IEEE Trans. Smart Grid

Author:

Wang Long^ORCID,Zhang Zijun^ORCID,Xu Jia,Liu Ruihua

Funder

General Research Fund from the Research Grants Council of the Hong Kong Special Administrative Region

CityU Strategic Research Grant

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/5165411/8388565/07676419.pdf?arnumber=7676419

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2. Wind turbine blade defect detection with a semi-supervised deep learning framework;Engineering Applications of Artificial Intelligence;2024-10

3. Transfer learning applications for autoencoder-based anomaly detection in wind turbines;Energy and AI;2024-09

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