Articles | Volume 10, issue 9
https://doi.org/10.5194/wes-10-2099-2025
https://doi.org/10.5194/wes-10-2099-2025
Research article
 | 
29 Sep 2025
Research article |  | 29 Sep 2025

In situ condition monitoring of floating offshorewind turbines using kurtosis anddeep-learning-based approaches

Adrien Hirvoas, Cesar Aguilera, Matthieu Perrault, Damien Desbordes, and Romain Ribault

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2024-189', Anonymous Referee #1, 03 Apr 2025
    • AC1: 'Reply on RC1', Adrien Hirvoas, 20 May 2025
  • RC2: 'Comment on wes-2024-189', Anonymous Referee #2, 22 Apr 2025
    • AC2: 'Reply on RC2', Adrien Hirvoas, 21 May 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Adrien Hirvoas on behalf of the Authors (16 Jun 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (17 Jun 2025) by Shawn Sheng
ED: Publish as is (28 Jun 2025) by Shawn Sheng
ED: Publish as is (30 Jun 2025) by Paul Veers (Chief editor)
AR by Adrien Hirvoas on behalf of the Authors (09 Jul 2025)  Manuscript 
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Short summary
This study compares two methods for detecting downtime in the Zefyros floating offshore wind turbine off Norway's coast. The first method uses kurtosis to find signal anomalies, while the second employs a deep-learning autoencoder to reduce and reconstruct data, identifying irregularities. Using 1 month of sensor data, the study finds the deep-learning method to be more accurate than kurtosis. This research advances the use of deep learning for effective wind turbine monitoring.
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