Wind Energy Science
Wind Energy Science
WES
Articles | Volume 11, issue 4
Articles | Volume 11, issue 4
https://doi.org/10.5194/wes-11-1363-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-1363-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 4
Research article
 | 
23 Apr 2026
Research article |  | 23 Apr 2026

Inferring wind turbine operational state and fatigue from high-frequency acceleration using self-supervised learning for SCADA (supervisory control and data acquisition)-free monitoring

Yacine Bel-Hadj, Francisco de Nolasco Santos, Wout Weijtjens, and Christof Devriendt

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Short summary
We show that simple vibration sensors on wind turbines can reveal how each machine is operating without relying on control system data. By learning patterns from short acceleration segments, our model identifies turbine behavior, detects changes in operation, and tracks events over time. These patterns also support estimating fatigue, providing a new way to understand turbine performance using only vibration measurements.
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