Wind Energy Science
Wind Energy Science
WES
Articles | Volume 10, issue 11
Articles | Volume 10, issue 11
https://doi.org/10.5194/wes-10-2615-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-2615-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 11
Research article
 | 
17 Nov 2025
Research article |  | 17 Nov 2025

Scalable SCADA-driven failure prediction for offshore wind turbines using autoencoder-based NBM and fleet-median filtering

Ivo Vervlimmeren, Xavier Chesterman, Timothy Verstraeten, Ann Nowé, and Jan Helsen

Viewed

Total article views: 855 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
682 131 42 855 30 53
  • HTML: 682
  • PDF: 131
  • XML: 42
  • Total: 855
  • BibTeX: 30
  • EndNote: 53
Views and downloads (calculated since 20 May 2025)
Cumulative views and downloads (calculated since 20 May 2025)

Viewed (geographical distribution)

Total article views: 855 (including HTML, PDF, and XML) Thereof 853 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 18 Nov 2025
Download
Short summary
We introduce a new method to refine failure prediction for wind turbines, leading to better and more efficient alarming. We do this by filtering detected anomalies based on the anomalies from the whole fleet. We compare submethods and find one that removes up to 65 % of detected anomalies while leaving the failure-predicting ones. We also detail how we trained the model that generated these anomalies and discuss the construction of the scalable pipeline that was used to deploy such models.
Read more
Share
Altmetrics
Final-revised paper
Preprint