Articles | Volume 11, issue 10
https://doi.org/10.5194/wes-11-3983-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
Research article
| 
23 Oct 2026
Research article |
| 23 Oct 2026
| 23 Oct 2026
A real-time IoT, LLM, AI-supported wind turbine failure prediction system
Cited articles
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Alzawaideh, B., Baboli, P. T., Babazadeh, D., Horodyvskyy, S., Koprek, I., and Lehnhoff, S.: Wind turbine failure prediction model using SCADA-based condition monitoring system, in: 2021 IEEE Madrid PowerTech, Madrid, Spain, 28 June–2 July 2021, 1–6, IEEE, https://doi.org/10.1109/PowerTech46648.2021.9495087, 2021.
Amin, A., Bibo, A., Panyam, M., and Tallapragada, P.: Vibration based fault diagnostics in a wind turbine planetary gearbox using machine learning, Wind Eng., 47, 175–189, https://doi.org/10.1177/0309524x221123968, 2022.
Corley, B., Koukoura, S., Carroll, J., and McDonald, A.: Combination of thermal modelling and machine learning approaches for fault detection in wind turbine gearboxes, Energies, 14, 1375, https://doi.org/10.3390/en14051375, 2021.
de Lima Munguba, C. F., Ochoa, A. A. V., Leite, G. D. N. P., da Costa, A. C. A., da Costa, J. Â. P., de Menezes, F. D., and de Souza, M. G. G.: Fault detection framework in wind turbine pitch systems using machine learning: Development, validation, and results, Eng. Appl. Artif. Intell., 138, 109307, https://doi.org/10.1016/j.engappai.2024.109307, 2024.
