Wind Energy Science
Wind Energy Science
WES
Articles | Volume 11, issue 4
Articles | Volume 11, issue 4
https://doi.org/10.5194/wes-11-1163-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-1163-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 4
Research article
 | 
10 Apr 2026
Research article |  | 10 Apr 2026

Sensor-error-robust normal-behavior modeling for wind turbine drive train failure prediction using a masked autoencoder

Xavier Chesterman, Ann Nowé, and Jan Helsen

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Short summary
This paper presents a sensor-error-robust methodology for failure prediction in wind turbines. Sensor malfunctions pose a significant challenge for data-driven prognostic approaches. The proposed method employs a masked autoencoder that enables selective deactivation of signals. Evaluation is done using data from several operational offshore wind farms. Results demonstrate that the model effectively mitigates the impact of sensor errors while maintaining high accuracy in failure prediction.
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