Wind Energy Science
Wind Energy Science
WES
Articles | Volume 10, issue 11
Articles | Volume 10, issue 11
https://doi.org/10.5194/wes-10-2563-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-2563-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 11
Research article
 | 
12 Nov 2025
Research article |  | 12 Nov 2025

Simulating run-to-failure SCADA time series to enhance wind turbine fault detection and prognosis

Ali Eftekhari Milani, Donatella Zappalá, Francesco Castellani, and Simon Watson

Viewed

Total article views: 2,804 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,378 336 90 2,804 79 102
  • HTML: 2,378
  • PDF: 336
  • XML: 90
  • Total: 2,804
  • BibTeX: 79
  • EndNote: 102
Views and downloads (calculated since 13 Jun 2025)
Cumulative views and downloads (calculated since 13 Jun 2025)

Viewed (geographical distribution)

Total article views: 2,804 (including HTML, PDF, and XML) Thereof 2,329 with geography defined and 475 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 14 Feb 2026
Download
Short summary
This paper proposes a data-driven approach to simulate wind turbine sensor time series, such as temperature and pressure signals, describing the behaviour of a wind turbine component as it degrades through time up to the failure point. It allows for the simulation of new failure events or the replication of a given failure under different conditions. The results show that the synthetic signals generated using this approach improve the performance of fault detection and prognosis methods.
Read more
Share
Altmetrics
Final-revised paper
Preprint