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https://doi.org/10.5194/wes-2025-168
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2025-168
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
29 Sep 2025
| 29 Sep 2025
Status: this preprint is currently under review for the journal WES.
Condition monitoring of wind turbine drivetrains: State-of-the-art technologies, recent trends, and future outlook
Abstract. As wind energy scales up to meet global decarbonization and energy security goals, reducing the LCoE has become essential, particularly through improvements in O&M. This positioning paper explores the state-of-the-art in condition monitoring for wind turbines. It focuses on drivetrain components, which are among the most failure-prone and maintenance-intensive subsystems. It examines current diagnostic and prognostic strategies using SCADA data, vibration and acoustic analysis, and digital twin frameworks, alongside emerging techniques in machine learning, signal processing, and hybrid modelling. The paper also identifies key challenges, including data availability, labelling, standardization, and the gap between academic research and industrial adoption. This work aims to guide future research and industrial efforts in making wind energy more reliable, predictable, and cost-efficient.
How to cite. Kestel, K., Chesterman, X., Zappalá, D., Watson, S., Li, M., Hart, E., Carroll, J., Vidal, Y., Nejad, A. R., Sheng, S., Guo, Y., Stammler, M., Wirsing, F., Saleh, A., Gregarek, N., Baszenski, T., Decker, T., Knops, M., Jacobs, G., Lehmann, B., König, F., Pereira, I., Daems, P.-J., Peeters, C., and Helsen, J.: Condition monitoring of wind turbine drivetrains: State-of-the-art technologies, recent trends, and future outlook, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2025-168, in review, 2025.
Competing interests: Yolanda Vidal, Yi Guo, Amir R. Nejad, and Shawn Sheng are members of the editorial board of Wind Energy Science.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.Download & links
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RC1: 'Comment on wes-2025-168', Anonymous Referee #1, 06 Nov 2025
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This is a very good, wide-ranging position paper on the subject. The group of distinguished authors have done an excellent job of coordinating such an informative work. It will be of interest to readers of the journal and should be published. Because it is so broad in scope, it is rather long. However, it is hard to see how any individual sections could be reduced. It might perhaps have been better published as a book, but if the journal board is happy with this format, so am I.It can be published as it is. I have suggested some minor points that may be of help to the authors.Section 1.1, p.2 – Consider starting with the formal definition of a drivetrain (it does appear later, so this is a minor point).Section 1.2.2 – A few references here would be useful.Section 1.2.2 – It would make sense to list these either in order of importance or in the order they appear in the drive train.Section 1.3.1 – How about presenting these levels as bullet points for quicker reading?L216–219 – It would be helpful to include a sentence giving examples of what these risks might be.L225 – Remove “etc.”L250–260 – This paragraph feels somewhat out of step with the paper. It is not clear what “Task 43” (and the later “second phase”) refers to, or what the organisational body is. This paragraph reads more as a description of task objectives and does not fit the overall narrative of the paper, which is about status.Section 3.1.1 – This is a comprehensive list of ML methodologies, but it lacks a clear takeaway message — e.g., which tools are best or most appropriate, or some guidelines for the operator. Table 1 is good and visual; could some of the extensive text in Sections 3.1.2 and 3.1.3 be replaced with a similarly well-crafted table?L727 – “This section has summarised…” use past tenseL731 – The phrase “according to the failure mode” — is it clear in the preceding section which steps are required for which expected failure mode?L1076, L1133 – Use the SCADA acronym, as it is now well established in the paper.L1425 – Autarkic is an uncommon word; I had to look it up. According to Wikipedia, autarky means self-sufficiency, usually applied to societies, communities, states, or economies. Is this the correct word in this context?L1462–1466 – The paper should report only work or concepts that have already been achieved, not future plans.Section 5.2.2 – CMS Signal Processing – The unnumbered sections on SAW and TFM do not fit well under this title (they are also not unique to gearboxes, which is the focus of Section 5.2). A lot of space is dedicated to SAW and TFM, which are two new sensor concepts, but very little is given to oil debris monitoring, which is well established. This feels out of balance.L1648 two periods ..ReplyCitation: https://doi.org/
10.5194/wes-2025-168-RC1
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Department of Applied Mechanics, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Offshore Wind Infrastructure Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Xavier Chesterman
Department of Applied Mechanics, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Offshore Wind Infrastructure Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
FlandersMake@VUB, Belgium
Artificial Intelligence Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Donatella Zappalá
TU Delft, Kluyverweg 1, Delft, 2629 HS, Netherlands
Simon Watson
TU Delft, Kluyverweg 1, Delft, 2629 HS, Netherlands
Mingxin Li
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, United Kingdom
Edward Hart
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, United Kingdom
James Carroll
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, United Kingdom
Yolanda Vidal
Control, Data, and Artificial Intelligence, Department of Mathematics, Escola d’Enginyeria de Barcelona Est, Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besós, Eduard Maristany, 16, 08019 Barcelona, Spain
Institute of Mathematics (IMTech) Universitat Politècnica de Catalunya (UPC) Barcelona 08028, Spain
Amir R. Nejad
Department of Marine Technology, Norwegian University of Science and Technology, NO-7491, Trondheim, Norway
Shawn Sheng
National Wind Technology Center, National Renewable Energy Laboratory, Golden, Colorado, USA
DTU Wind and Energy Systems, Wind Energy Materials and Components Division, Frederiksborgvej 399, 4000 Roskilde, Denmark
Matthias Stammler
DTU Wind and Energy Systems, Wind Energy Materials and Components Division, Frederiksborgvej 399, 4000 Roskilde, Denmark
Fraunhofer IWES, Large Bearing Laboratory, Am Schleusengraben 22, 21029 Hamburg, Germany
Florian Wirsing
Institute for Machine Elements and Systems Engineering, RWTH Aachen University, Schinkelstraße 10, 52062 Aachen, Germany
Ahmed Saleh
Institute for Machine Elements and Systems Engineering, RWTH Aachen University, Schinkelstraße 10, 52062 Aachen, Germany
Nico Gregarek
Institute for Machine Elements and Systems Engineering, RWTH Aachen University, Schinkelstraße 10, 52062 Aachen, Germany
Thao Baszenski
Institute for Machine Elements and Systems Engineering, RWTH Aachen University, Schinkelstraße 10, 52062 Aachen, Germany
Thomas Decker
Center for Wind Power Drives, RWTH Aachen University, Campus-Boulevard 61, 52074 Aachen, Germany
Martin Knops
Center for Wind Power Drives, RWTH Aachen University, Campus-Boulevard 61, 52074 Aachen, Germany
Georg Jacobs
Institute for Machine Elements and Systems Engineering, RWTH Aachen University, Schinkelstraße 10, 52062 Aachen, Germany
Center for Wind Power Drives, RWTH Aachen University, Campus-Boulevard 61, 52074 Aachen, Germany
Benjamin Lehmann
Institute for Machine Elements and Systems Engineering, RWTH Aachen University, Schinkelstraße 10, 52062 Aachen, Germany
Florian König
Institute for Machine Elements and Systems Engineering, RWTH Aachen University, Schinkelstraße 10, 52062 Aachen, Germany
Ines Pereira
Department of Applied Mechanics, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Offshore Wind Infrastructure Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Pieter-Jan Daems
Department of Applied Mechanics, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Offshore Wind Infrastructure Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Cédric Peeters
Department of Applied Mechanics, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Offshore Wind Infrastructure Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
FlandersMake@VUB, Belgium
Jan Helsen
Department of Applied Mechanics, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Offshore Wind Infrastructure Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
FlandersMake@VUB, Belgium
Short summary
Wind energy use has been rapidly expanding worldwide in recent years. Driven by global decarbonization goals and energy security concerns, this growth is expected to continue. To achieve these targets, production costs must decrease, with operation and maintenance being major contributors. This paper reviews current and emerging technologies for monitoring wind turbine drivetrains and highlights key academic and industrial challenges that may hinder progress.
Wind energy use has been rapidly expanding worldwide in recent years. Driven by global...
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