Wind Energy Science
Wind Energy Science
WES
Articles | Volume 10, issue 11
Articles | Volume 10, issue 11
https://doi.org/10.5194/wes-10-2615-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-2615-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 11
Research article
 | 
17 Nov 2025
Research article |  | 17 Nov 2025

Scalable SCADA-driven failure prediction for offshore wind turbines using autoencoder-based NBM and fleet-median filtering

Ivo Vervlimmeren, Xavier Chesterman, Timothy Verstraeten, Ann Nowé, and Jan Helsen

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Cited articles

Beretta, M., Cárdenas, J. J., Koch, C., and Cusidó, J.: Wind fleet generator fault detection via SCADA alarms and autoencoders, Applied Sciences, 10, 8649, https://doi.org/10.3390/app10238649, 2020. a, b
Black, I. M., Richmond, M., and Kolios, A.: Condition monitoring systems: a systematic literature review on machine-learning methods improving offshore-wind turbine operational management, International Journal of Sustainable Energy, 40, 923–946, 2021. a, b
Canizo, M., Onieva, E., Conde, A., Charramendieta, S., and Trujillo, S.: Real-time predictive maintenance for wind turbines using Big Data frameworks, in: 2017 ieee international conference on prognostics and health management (icphm), pp. 70–77, IEEE, 2017. a
Carroll, J., McDonald, A., and McMillan, D.: Failure rate, repair time and unscheduled O&M cost analysis of offshore wind turbines, Wind energy, 19, 1107–1119, 2016. a
Chen, H., Liu, H., Chu, X., Liu, Q., and Xue, D.: Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network, Renewable Energy, 172, 829–840, 2021. a
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We introduce a new method to refine failure prediction for wind turbines, leading to better and more efficient alarming. We do this by filtering detected anomalies based on the anomalies from the whole fleet. We compare submethods and find one that removes up to 65 % of detected anomalies while leaving the failure-predicting ones. We also detail how we trained the model that generated these anomalies and discuss the construction of the scalable pipeline that was used to deploy such models.
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