Wind Energy Science
Wind Energy Science
WES
Articles | Volume 10, issue 9
Articles | Volume 10, issue 9
https://doi.org/10.5194/wes-10-1963-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-1963-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 9
Research article
 | 
11 Sep 2025
Research article |  | 11 Sep 2025

Leveraging signal processing and machine learning for automated fault detection in wind turbine drivetrains

Faras Jamil, Cédric Peeters, Timothy Verstraeten, and Jan Helsen

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Cited articles

Antoni, J.: Fast computation of the kurtogram for the detection of transient faults, Mech. Syst. Signal Pr., 21, 108–124, 2007. a
Antoni, J.: Cyclostationarity by examples, Mech. Syst. Signal Pr., 23, 987–1036, https://doi.org/10.1016/j.ymssp.2008.10.010, 2009. a
Antoni, J.: A critical overview of the “Filterbank-Feature-Decision” methodology in machine condition monitoring, Acoust. Aust., 49, 177–184, 2021. a
Antoni, J. and Borghesani, P.: A statistical methodology for the design of condition indicators, Mech. Syst. Signal Pr., 114, 290–327, 2019. a
Antoni, J. and Randall, R.: Unsupervised noise cancellation for vibration signals: part I – evaluation of adaptive algorithms, Mech. Syst. Signal Pr., 18, 89–101, 2004. a
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A hybrid fault detection method is proposed, which combines physical domain knowledge with machine learning models to automatically detect mechanical faults in wind turbine drivetrain components. It offers detailed insights for experts while giving operators a high-level overview of the machine's health to assist in planning effective maintenance strategies. It was validated on multiple years of wind farm data and the potential faults were accurately predicted, which was confirmed by experts.
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