the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Continuous lifetime monitoring technique for structural components and main bearings in wind turbines based on measured strain and virtual load sensors
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.- Preprint
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RC1: 'Comment on wes-2025-233', Anonymous Referee #1, 21 Nov 2025
The comment was uploaded in the form of a supplement: https://wes.copernicus.org/preprints/wes-2025-233/wes-2025-233-RC1-supplement.pdfCitation: https://doi.org/
10.5194/wes-2025-233-RC1 -
RC2: 'Comment on wes-2025-233', Anonymous Referee #2, 03 Dec 2025
This paper presents an interesting approach to continuous lifetime monitoring of wind turbine towers and main bearings using strain gauge calibration and virtual load sensors over nearly 10 years of data. The methodology is comprehensive and the long-term dataset is valuable. However, several clarifications and corrections are needed before publication, as outlined below.
- The paper is quite long as another reviewer has also noted. Consider shortening by consolidating or removing redundant material. Some sections could be more concise.
- The paper switches inconsistently between "nearly 10 years," "9 years," and "7.5 years" when describing different analyses. A clear timeline figure would help readers follow which period is being discussed in each section.
- The gearbox mounting stiffness of 20×10^6 N/m is from literature for different turbines. The sensitivity analysis shows this can cause 60% error in P_eq. How representative is this value for your specific drivetrain? How could this be validated in practice?
- The hyperparameter tuning used only 5 hours of data compared to 160 hours for training. Why do the authors use such a small amount? This seems inconsistent.
- You trained on 160 hours but validated on 160 hours from 2019. How was validation set selected? Same k-means or random?
- There's a fundamental clarity issue with the lifetime calculations in section 5.2.1 and Figure 7. The accumulated damage values shown in the figure don't align with the stated lifetimes of 166 years for the front main bearing and 333 years for the rear main bearing. The same issue appears for the tower bottom lifetime. Please verify and clarify these numbers.
- Figure 12b shows the correction factor varies across years for different inputs. Using average value could introduce bias depending on which year dominates. Did the authors check if this correlates with annual wind statistics?
- Figure 11 shows all virtual sensors systematically underpredict tower damage. The proposed correction method needs 6 months to 1 year of real measurements for calibration. This limits the benefit and you still need substantial measurements. What's minimum period needed for reliable correction?
- LSTM with strain inputs (no accelerometer) performed worse than simpler FNN. Needs more explanation than just "LSTM cannot attenuate 1P, 2P, 3P contributions." Why would temporal modeling perform worse with these periodic signals?
- Low turbulence increasing bearing loads at rated wind speed is interesting but not sufficiently explained. The authors cite HIPERWIND D5.4 but no intuition provided. Could this be because control system switches between modes frequently with turbulent inflow, leading to peak thrust? (see https://doi.org/10.1115/1.4041996)
Citation: https://doi.org/10.5194/wes-2025-233-RC2 -
AC1: 'Comment on wes-2025-233', Bruno Rodrigues Faria, 22 Jan 2026
Dear Editor and Reviewers,
The authors sincerely appreciate your time and the constructive feedback provided on our preprint published in the WES journal.
We have addressed the reviewer comments and incorporated the suggested changes to produce a more coherent and well-structured revised manuscript. In addition, the revised manuscripthas benefited from further grammatical editing and minor revisions to reduce its length while preserving the clarity and integrity of the key findings.
The attached file includes an overview and a detailed response to the reviewers’ comments, along with a marked-up version of the manuscript highlighting all changes made to the preprint.
Kind regards,
Bruno Faria and co-authors
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