Wind Energy Science
Wind Energy Science
WES
Articles | Volume 10, issue 12
Articles | Volume 10, issue 12
https://doi.org/10.5194/wes-10-2841-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-2841-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 12
Research article
 | 
01 Dec 2025
Research article |  | 01 Dec 2025

Fault detection in wind turbines using health index monitoring with variational autoencoders

Shun Wang, Yolanda Vidal, and Francesc Pozo

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Short summary
This research aims to improve wind turbine maintenance by detecting faults early using only data from normal operation. The method analyzes vibration signals in both time and frequency domains and uses a variational autoencoder, a type of deep learning model, to learn normal behavior. It then detects anomalies by measuring how much new data deviate from this learned model. Tests on real turbine data show early and accurate detection of faults such as pitch issues and icing.
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