Articles | Volume 11, issue 4
https://doi.org/10.5194/wes-11-1363-2026
https://doi.org/10.5194/wes-11-1363-2026
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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Cited articles

Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., and Marchand, M.: Domain-adversarial neural networks, arXiv preprint arXiv:1412.4446, https://doi.org/10.48550/arXiv.1412.4446, 2014. a
Avendano-Valencia, L. D., Chatzi, E. N., and Tcherniak, D.: Gaussian process models for mitigation of operational variability in the structural health monitoring of wind turbines, Mech. Syst. Signal Pr., 142, 106686, https://doi.org/10.1016/j.ymssp.2020.106686, 2020. a
Bel-Hadj, Y.: YacineBelHadj/operational_state_from_autoencoder: operational_state_from_autoencoder_WES (v1.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.19439517, 2026a. a
Bel-Hadj, Y.: YacineBelHadj/dem_from_acceleration: DEM_from_acceleration (v1.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.19440161, 2026b. a
Bel-Hadj, Y. and Weijtjens, W.: Anomaly detection in vibration signals for structural health monitoring of an offshore wind turbine, in: European Workshop on Structural Health Monitoring, pp. 348–358, Springer, https://doi.org/10.1007/978-3-031-07322-9_36, 2022. a
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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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