Articles | Volume 10, issue 5
https://doi.org/10.5194/wes-10-857-2025
https://doi.org/10.5194/wes-10-857-2025
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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Cited articles

Adler, J. and Öktem, O.: Solving ill-posed inverse problems using iterative deep neural networks, Inverse Probl., 33, 124007, https://doi.org/10.1088/1361-6420/aa9581, 2017. a
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Aldirany, Z.: Accurate Approximations of the Wave Equation: From Spectral Element Methods to Deep Learning Approaches, PhD thesis, University of Montreal, Montreal, Canada, https://publications.polymtl.ca/58312/ (last access: 8 April 2025​​​​​​​), 2024. a
Alyaev, S. and Elsheikh, A. H.: Direct multi-modal inversion of geophysical logs using deep learning, Earth and Space Science, 9, e2021EA002186, https://doi.org/10.1029/2021EA002186, 2022. a
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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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