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.
Research article
| 
01 Apr 2026
Research article |
| 01 Apr 2026
| 01 Apr 2026
Failure classification of wind turbine operational conditions using hybrid machine learning
Related authors
Wind Energ. Sci., 11, 2801–2816, 2026
Cited articles
Amin, A., Bibo, A., Panyam, M., and Tallapragada, P.: Vibration based fault diagnostics in a wind turbine planetary gearbox using machine learning, Wind Engineering, 47, 175–189, https://doi.org/10.1177/0309524X221123968, 2023. a
Ángel Encalada-Dávila, Vidal, Y., and Tutivén, C.: Strain virtual sensor for offshore wind turbine jacket supports: A time series transformer approach validated with Alpha Ventus wind farm data, Mech. Syst. Signal Pr., 231, 112653, https://doi.org/10.1016/j.ymssp.2025.112653, 2025. a
Antoniadou, I., Dervilis, N., Barthorpe, R. J., Manson, G., and Worden, K.: Advanced tools for damage detection in wind turbines, Key Engineering Materials, 569–570, 547–554, https://doi.org/10.4028/www.scientific.net/KEM.569-570.547, 2013. a
Antoniadou, I., Dervilis, N., Papatheou, E., Maguire, A. E., and Worden, K.: Aspects of structural health and condition monitoring of offshore wind turbines, Philos. T. R. Soc. A, 373, https://doi.org/10.1098/rsta.2014.0075, 2015a. a
Antoniadou, I., Dervilis, N., Papatheou, E., Maguire, A. E., and Worden, K.: Aspects of structural health and condition monitoring of offshore wind turbines, Philos. T. R. Soc. A, 373, 20140075, https://doi.org/10.1098/rsta.2014.0075, 2015b. a
