Articles | Volume 11, issue 3
https://doi.org/10.5194/wes-11-1057-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-1057-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Apr 2026
Research article |
| 01 Apr 2026
| 01 Apr 2026
Failure classification of wind turbine operational conditions using hybrid machine learning
Marcela Rodrigues Machado
CORRESPONDING AUTHOR
Department of Mechanical Engineering, University of Brasília, Campus Universitário Darcy Ribeiro, Brasília, 70910-900, Brazil
Amanda Aryda Silva Rodrigues de Sousa
Department of Mechanical Engineering, University of Brasília, Campus Universitário Darcy Ribeiro, Brasília, 70910-900, Brazil
Université Marie et Louis Pasteur, SUPMICROTECH, CNRS, Institut FEMTO-ST, 25000 Besançon, France
Jefferson da Silva Coelho
Department of Mechanical Engineering, University of Brasília, Campus Universitário Darcy Ribeiro, Brasília, 70910-900, Brazil
Department of Mechanical Engineering, Federal University of Amazonas, 69080-900, Manaus, Brazil
Rafael de Oliveira Teloli
Université Marie et Louis Pasteur, SUPMICROTECH, CNRS, Institut FEMTO-ST, 25000 Besançon, France
Related authors
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Preprint under review for WES
Short summary
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Open innovation digital platforms can boost data sharing and maturity in wind energy. This study applies a two-phase Design Thinking approach to evolve the WeDoWind project into an open innovation ecosystem. A feasibility study using a fault detection challenge showed medium feasibility: strong governance and scalability, but improvements are needed in funding, adoption, and community engagement for sustainable growth.
Sarah Barber, Shun Wang, Francesc Pozo, Yolanda Vidal, Marcela Rodrigues Machado, Amanda Aryda Silva Rodrigues de Sousa, Jefferson da Silva Coelho, Xukai Zhang, Yao-Teng Hu, Arash Noshadravan, Theodoros Varouxis, Mahmoud Abdelhak, Ramin Ghiasi, and Abdollah Malekjafarian
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-122, https://doi.org/10.5194/wes-2025-122, 2025
Preprint under review for WES
Short summary
Short summary
Open innovation digital platforms can boost data sharing and maturity in wind energy. This study applies a two-phase Design Thinking approach to evolve the WeDoWind project into an open innovation ecosystem. A feasibility study using a fault detection challenge showed medium feasibility: strong governance and scalability, but improvements are needed in funding, adoption, and community engagement for sustainable growth.
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Short summary
We have developed a method using artificial intelligence to detect and classify faults in wind turbines before major damage occurs. By analyzing data from multiple sensors, we can identify issues even under changing weather conditions, such as temperature and wind. This improves reliability, reduces downtime, and lowers maintenance costs, supporting cleaner and more affordable energy through stable production.
We have developed a method using artificial intelligence to detect and classify faults in wind...
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