Wind Energy Science
Wind Energy Science
WES
Articles | Volume 10, issue 5
Articles | Volume 10, issue 5
https://doi.org/10.5194/wes-10-857-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-857-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 5
Research article
 | 
06 May 2025
Research article |  | 06 May 2025

Gaussian mixture autoencoder for uncertainty-aware damage identification in a floating offshore wind turbine

Ana Fernandez-Navamuel, Nicolas Gorostidi, David Pardo, Vincenzo Nava, and Eleni Chatzi

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Short summary
This work employs deep neural networks to identify damage in the mooring system of a floating offshore wind turbine using measurements from the platform response. We account for the effect of uncertainty caused by the existence of multiple solutions using a Gaussian mixture model to describe the damage condition estimates. The results reveal the capability of the methodology to discover the uncertainty in the assessment, which increases as the instrumentation system becomes more limited.
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