Preprints
https://doi.org/10.5194/wes-2025-233
https://doi.org/10.5194/wes-2025-233
11 Nov 2025
 | 11 Nov 2025
Status: this preprint is currently under review for the journal WES.

Continuous lifetime monitoring technique for structural components and main bearings in wind turbines based on measured strain and virtual load sensors

Bruno Rodrigues Faria, Nikolay Dimitrov, Nikhil Sudhakaran, Matthias Stammler, Athanasios Kolios, W. Dheelibun Remigius, Xiaodong Zhang, and Asger Bech Abrahamsen

Abstract. Decisions on the lifetime extension of wind turbines require evaluating the remaining useful life of major load-carrying components by making a comparison to the design lifetime. This work focuses on the lifetime assessment of two fundamentally different components: a structural component in the form of the tower and rotating components in the form of the main bearings. A method is presented that combines high-frequency SCADA, accelerometers, minimally intrusive strain gauge at blade and tower, and limited design information for continued estimates of the component loads and their subsequent fatigue damage accumulations. The work is applied to a highly instrumented DTU research turbine, a Vestas V52 model, where strain gauges in the blade root and in the tower bottom are calibrated for nearly 10 years using continual calibration methods without the need for operator input. The lifetime estimates of the tower bottom and front and rear main bearings were found to be 1770 years and 166–333 years, respectively, reflecting the low average wind speed of the turbine site compared to the wind turbine design wind class IA. Secondly, it was investigated whether virtual load sensors can replace tower strain gauges and if one can use only uptower sensors for lifetime evaluation. Consistent tower bottom strain signal estimate and long-term damage accumulation were achieved with ±5 % lifetime variability once SCADA, nacelle accelerometers, and blade root strain gauges were combined for the deployment of a long short-term memory (LSTM) neural network. A systematic underprediction of the accumulated damage of the tower bottom was observed for the virtual load sensors, and a correction method was proposed. Finally, the impact of environmental conditions, including turbulence intensity and shear exponent of the incoming wind, on the main bearing lifetime was investigated using 10 years of measurements. A simple drivetrain thermal model was used to evaluate the modified lifetime L10m of the main bearings, depending on the measured ambient temperature and the grease cleanliness assumptions. Higher fatigue loads are observed on the main bearings at rated wind speeds with low turbulence intensity and low shear. Changes of ±5 °C in the ambient temperature around 15 °C caused a 10-year difference in the operational life of the main bearings at rated wind speed. It was also found that the specification of the gearbox mounting stiffness can lead to a 60 % overprediction of the main bearing loads.

Competing interests: Some authors are members of the editorial board of journal Wind Energy Science (WES).

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.
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Bruno Rodrigues Faria, Nikolay Dimitrov, Nikhil Sudhakaran, Matthias Stammler, Athanasios Kolios, W. Dheelibun Remigius, Xiaodong Zhang, and Asger Bech Abrahamsen

Status: open (until 09 Dec 2025)

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Bruno Rodrigues Faria, Nikolay Dimitrov, Nikhil Sudhakaran, Matthias Stammler, Athanasios Kolios, W. Dheelibun Remigius, Xiaodong Zhang, and Asger Bech Abrahamsen
Bruno Rodrigues Faria, Nikolay Dimitrov, Nikhil Sudhakaran, Matthias Stammler, Athanasios Kolios, W. Dheelibun Remigius, Xiaodong Zhang, and Asger Bech Abrahamsen

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Short summary
This study demonstrates lifetime assessments of a wind turbine structural component as the tower and rotating components as the main bearings using the controller data, measurements, and no blade design information, representing a realistic scenario for operating turbines. A tower bottom virtual load sensor framework based on neural networks was proposed using different input combinations to replace the tower sensor. The estimated lifetime was considerably longer than the design lifetime.
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