Articles | Volume 11, issue 4
https://doi.org/10.5194/wes-11-1363-2026
© Author(s) 2026. 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-11-1363-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Inferring wind turbine operational state and fatigue from high-frequency acceleration using self-supervised learning for SCADA (supervisory control and data acquisition)-free monitoring
OWI-Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Elsene, Belgium
Francisco de Nolasco Santos
OWI-Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Elsene, Belgium
Wout Weijtjens
OWI-Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Elsene, Belgium
Christof Devriendt
OWI-Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Elsene, Belgium
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Negin Sadeghi, Pablo G. Morato, Nymfa Noppe, Nandar Hlaing, Wout Weijtjens, and Christof Devriendt
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-65, https://doi.org/10.5194/wes-2026-65, 2026
Preprint under review for WES
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We studied how stationary the long-term damage estimates are when based on only short periods of measurement in offshore wind turbines. Using eight years of real data, we compared many one‑year measurement windows and showed that results can differ strongly depending on which year is used, even when current uncertainty methods suggest high confidence. So short measurements may not represent long-term behaviour, if proper conditioning to environmental and operational conditions is not done.
Ahmed Mujtaba, Wout Weijtjens, Negin Sadeghi, and Christof Devriendt
Wind Energ. Sci., 11, 443–467, https://doi.org/10.5194/wes-11-443-2026, https://doi.org/10.5194/wes-11-443-2026, 2026
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This study proposes a random-forest-based machine learning (ML) model for fatigue life prediction of offshore wind turbine monopile foundations compared to traditional approaches, using long-term strain data from turbines in the Belgian North Sea. This study shows that the ML model predicts the fatigue life of monopile foundations more reliably when only short-term measurements are available, whereas for longer monitoring periods of greater than 12 months, simpler binning methods perform equally well.
Negin Sadeghi, Pietro D'Antuono, Nymfa Noppe, Koen Robbelein, Wout Weijtjens, and Christof Devriendt
Wind Energ. Sci., 8, 1839–1852, https://doi.org/10.5194/wes-8-1839-2023, https://doi.org/10.5194/wes-8-1839-2023, 2023
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Analysis of long-term fatigue damage of four offshore wind turbines using 3 years of measurement data was performed for the first time to gain insight into the low-frequency fatigue damage (LFFD) impact on overall consumed life. The LFFD factor depends on the (linear) stress–life (SN) curve slope, heading, site, signal, and turbine type. Up to ∼ 65 % of the total damage can be related to LFFDs. Therefore, in this case study, the LFFD effect has a significant impact on the final damage.
Francisco d N Santos, Nymfa Noppe, Wout Weijtjens, and Christof Devriendt
Wind Energ. Sci., 7, 299–321, https://doi.org/10.5194/wes-7-299-2022, https://doi.org/10.5194/wes-7-299-2022, 2022
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The estimation of fatigue in offshore wind turbine is highly relevant, as it can help extend the lifetime of these assets. This article attempts to answer this issue by developing a methodology based on artificial intelligence and data collected by sensors installed in real-world turbines. Good results are obtained, and this methodology is further used to learn the value of eight different sensor setups and employed in a real-world wind farm with 48 wind turbines.
Cited articles
Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., and Marchand, M.: Domain-adversarial neural networks, arXiv preprint arXiv:1412.4446, https://doi.org/10.48550/arXiv.1412.4446, 2014. a
Avendano-Valencia, L. D., Chatzi, E. N., and Tcherniak, D.: Gaussian process models for mitigation of operational variability in the structural health monitoring of wind turbines, Mech. Syst. Signal Pr., 142, 106686, https://doi.org/10.1016/j.ymssp.2020.106686, 2020. a
Bel-Hadj, Y.: YacineBelHadj/operational_state_from_autoencoder: operational_state_from_autoencoder_WES (v1.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.19439517, 2026a. a
Bel-Hadj, Y.: YacineBelHadj/dem_from_acceleration: DEM_from_acceleration (v1.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.19440161, 2026b. a
Bel-Hadj, Y. and Weijtjens, W.: Anomaly detection in vibration signals for structural health monitoring of an offshore wind turbine, in: European Workshop on Structural Health Monitoring, pp. 348–358, Springer, https://doi.org/10.1007/978-3-031-07322-9_36, 2022. a
Bel-Hadj, Y., Weijtjens, W., and de Nolasco Santos, F.: Anomaly detection and representation learning in an instrumented railway bridge, in: ESANN, https://doi.org/10.14428/esann/2022.ES2022-29, 2022. a, b
Bel-Hadj, Y., Weijtjens, W., and Devriendt, C.: Structural health monitoring in a population of similar structures with self-supervised learning: a two-stage approach for enhanced damage detection and model tuning, Struct. Health Monit., p. 14759217251324194, https://doi.org/10.1177/14759217251324194, 2025. a
Bengio, Y., Courville, A., and Vincent, P.: Representation learning: A review and new perspectives, IEEE T. Pattern Anal., 35, 1798–1828, https://doi.org/10.1109/TPAMI.2013.50, 2013. a, b
Bette, H. M., Wiedemann, C., Wächter, M., Freund, J., Peinke, J., and Guhr, T.: Dynamics of wind turbine operational states, arXiv preprint arXiv:2310.06098, https://doi.org/10.48550/arXiv.2310.06098, 2023. a
Bull, L. A., Gardner, P. A., Gosliga, J., Dervilis, N., Papatheou, E., Maguire, A. E., Campos, C., Rogers, T. J., Cross, E. J., and Worden, K.: Towards population-based structural health monitoring, Part I: Homogeneous populations and forms, in: Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics 2020, pp. 287–302, Springer, https://doi.org/10.1007/978-3-030-47638-0_32, 2020. a, b
Büth, C. M., Acharya, K., and Zanin, M.: infomeasure: a comprehensive Python package for information theory measures and estimators, Sci. Rep., 15, 29323, https://doi.org/10.48550/arXiv.2505.14696, 2025. a
Byrne, B. W., Burd, H. J., Zdravković, L., McAdam, R. A., Taborda, D. M., Houlsby, G. T., Jardine, R. J., Martin, C. M., Potts, D. M., and Gavin, K. G.: PISA: new design methods for offshore wind turbine monopiles, Revue Française de Géotechnique, p. 3, https://doi.org/10.1051/geotech/2019009, 2019. a
Chu, J.-C., Yuan, L., Xie, F., Pan, L., Wang, X.-D., and Zhang, L.-Z.: Operational State Analysis of Wind Turbines Based on SCADA Data, in: 2nd International Conference on Electrical and Electronic Engineering (EEE 2019), pp. 169–173, Atlantis Press, https://doi.org/10.2991/eee-19.2019.29, 2019. a, b
Contreras, P. and Murtagh, F.: Hierarchical clustering, Handbook of cluster analysis, pp. 103–123, https://doi.org/10.1201/b19706-11, 2015. a
Cooley, J. W. and Tukey, J. W.: An algorithm for the machine calculation of complex Fourier series, Math. Comput., 19, 297–301, 1965. a
Daems, P.-J., Peeters, C., Matthys, J., Verstraeten, T., and Helsen, J.: Fleet-wide analytics on field data targeting condition and lifetime aspects of wind turbine drivetrains, Forsch. Ingenieurwes., 87, 285–295, 2023. a
d N Santos, F., Noppe, N., Weijtjens, W., and Devriendt, C.: Data-driven farm-wide fatigue estimation on jacket-foundation OWTs for multiple SHM setups, Wind Energ. Sci., 7, 299–321, https://doi.org/10.5194/wes-7-299-2022, 2022. a, b, c
de N Santos, F., D’Antuono, P., Robbelein, K., Noppe, N., Weijtjens, W., and Devriendt, C.: Long-term fatigue estimation on offshore wind turbines interface loads through loss function physics-guided learning of neural networks, Renew. Energ., 205, 461–474, 2023. a
de Nolasco Santos, F., Bel-Hadj, Y., Weijtjens, W., and Devriendt, C.: Estimating Fatigue Through Latent Space Embedding of Acceleration in Offshore Wind Turbines, in: International Conference on Experimental Vibration Analysis for Civil Engineering Structures, pp. 943–951, Springer, https://doi.org/10.1007/978-3-031-96106-9_96, 2025. a
Ganin, Y. and Lempitsky, V.: Unsupervised domain adaptation by backpropagation, in: International conference on machine learning, pp. 1180–1189, PMLR, https://doi.org/10.48550/arXiv.1409.7495, 2015. a
Gardner, P., Bull, L. A., Gosliga, J., Poole, J., Dervilis, N., and Worden, K.: A population-based SHM methodology for heterogeneous structures: Transferring damage localisation knowledge between different aircraft wings, Mech. Syst. Signal Pr., 172, 108918, https://doi.org/10.1016/j.ymssp.2022.108918, 2022. a
Ha, D. and Schmidhuber, J.: Recurrent world models facilitate policy evolution, Adv. Neur. In., 31, https://doi.org/10.48550/arXiv.1809.01999, 2018. a
Hameed, Z., Hong, Y. S., Cho, Y. M., Ahn, S. H., and Song, C. K.: Condition monitoring and fault detection of wind turbines and related algorithms: A review, Adv. Mater. Res.-Switz., 13, 1–39, 2009. a
Hinton, G. E. and Salakhutdinov, R. R.: Reducing the dimensionality of data with neural networks, Science, 313, 504–507, https://doi.org/10.1126/science.1127647, 2006. a
Hlaing, N., Morato, P. G., Santos, F. d. N., Weijtjens, W., Devriendt, C., and Rigo, P.: Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks, Struct. Health Monit., 23, 1641–1663, 2024. a
IEC 61400-1: Wind energy generation systems – Part 1: Design requirements, https://webstore.iec.ch/publication/26423 (last access: 6 April 2026), 2019. a
Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980, https://doi.org/10.48550/arXiv.1412.6980, 2014. a
Korkos, P., Linjama, M., Kleemola, J., and Lehtovaara, A.: Data annotation and feature extraction in fault detection in a wind turbine hydraulic pitch system, Renew. Energ., 185, 692–703, 2022. a
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444, https://doi.org/10.1038/nature14539, 2015. a
Li, Z., Liu, Y., and Xia, Y.: Damage detection of bridges subjected to moving load based on domain-adversarial neural network considering measurement and model error, Eng. Struct., 293, 116601, https://doi.org/10.1016/j.engstruct.2023.116601, 2023. a
Li, Z., Chen, Y., Xu, T., and Huang, H.: Cross-domain damage detection through partial conditional adversarial domain adaptation, Mech. Syst. Signal Pr., 225, 110118, https://doi.org/10.1016/j.ymssp.2025.110118, 2025. a
Liu, D., Wang, T., Liu, S., Wang, R., Yao, S., and Abdelzaher, T.: Contrastive self-supervised representation learning for sensing signals from the time-frequency perspective, in: IEEE International Conference on Computer Communications (INFOCOM Workshops), pp. 1–6, IEEE, https://doi.org/10.1109/ICCCN52240.2021.9522151, 2021. a
Mao, W., He, J., Li, Y., and Yan, Y.: A new structured domain adversarial neural network for transfer fault diagnosis of rolling bearings under different working conditions, IEEE T. Instrum. Meas., 70, 1–13, https://doi.org/10.1109/TIM.2020.3038596, 2020. a
Martakis, P., Chatzi, E., Michalis, I., and Karapetrou, S.: Fusing damage-sensitive features and domain adaptation towards robust damage classification in real buildings, Soil Dyn. Earthq. Eng., 166, 107739, https://doi.org/10.3929/ethz-b-000593193, 2023. a
McInnes, L., Healy, J., and Melville, J.: UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, arXiv preprint arXiv:1802.03426, https://doi.org/10.48550/arXiv.1802.03426, 2018. a
Ozturkoglu, O., Ozcelik, O., and Günel, S.: Effects of Operational and Environmental Conditions on Estimated Dynamic Characteristics of a Large In-service Wind Turbine, J. Vib. Eng. Technol., 12, 803–824, 2024. a
Rahimi Taghanaki, F. et al.: Self-supervised human activity recognition with localized time-frequency contrastive representation learning, in: Proceedings of the 30th ACM International Conference on Multimedia, ACM, https://doi.org/10.1145/3581783.3612063, 2023. a
Ranzato, M., Monga, R., Devin, M., Chen, K., Corrado, G., Dean, J., Le, Q. V., and Ng, A. Y.: Building high-level features using large scale unsupervised learning, in: Proceedings of the 29th International Conference on Machine Learning (ICML-12), pp. 81–88, https://doi.org/10.48550/arXiv.1112.6209, 2012. a
Singh, D., Dwight, R., and Viré, A.: Probabilistic surrogate modeling of damage equivalent loads on onshore and offshore wind turbines using mixture density networks, Wind Energ. Sci., 9, 1885–1904, https://doi.org/10.5194/wes-9-1885-2024, 2024. a
Snover, D.: Urban Seismic Noise Identified with Deep Embedded Clustering Using a Dense Array in Long Beach, CA, Master's thesis, University of California San Diego, https://noiselab.ucsd.edu/group/Thesis/DSnover_MastersThesis.pdf (last access: 6 April 2026), 2020. a
Soares-Ramos, E. P., de Oliveira-Assis, L., Sarrias-Mena, R., and Fernández-Ramírez, L. M.: Current status and future trends of offshore wind power in Europe, Energy, 202, 117787, https://doi.org/10.1016/j.energy.2020.117787, 2020. a
Tschannen, M., Bachem, O., and Lucic, M.: Recent advances in autoencoder-based representation learning, arXiv preprint arXiv:1812.05069, https://doi.org/10.48550/arXiv.1812.05069, 2018. a
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.-A., and Bottou, L.: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, J. Mach. Learn. Res., 11, https://www.jmlr.org/papers/volume11/vincent10a/vincent10a.pdf (last access: 6 April 2026), 2010. a
Weijtens, W., Noppe, N., Verbelen, T., Iliopoulos, A., and Devriendt, C.: Offshore wind turbine foundation monitoring, extrapolating fatigue measurements from fleet leaders to the entire wind farm, in: Journal of Physics: Conference Series, vol. 753, p. 092018, IOP Publishing, https://doi.org/10.1088/1742-6596/753/9/092018, 2016. a, b
Xie, J., Girshick, R., and Farhadi, A.: Unsupervised deep embedding for clustering analysis, in: International conference on machine learning, pp. 478–487, PMLR, https://doi.org/10.48550/arXiv.1511.06335, 2016. a, b, c
Zhao, Y., Pan, J., Huang, Z., Miao, Y., Jiang, J., and Wang, Z.: Analysis of vibration monitoring data of an onshore wind turbine under different operational conditions, Eng. Struct., 205, 110071, https://doi.org/10.1016/j.engstruct.2019.110071, 2020. a
Short summary
We show that simple vibration sensors on wind turbines can reveal how each machine is operating without relying on control system data. By learning patterns from short acceleration segments, our model identifies turbine behavior, detects changes in operation, and tracks events over time. These patterns also support estimating fatigue, providing a new way to understand turbine performance using only vibration measurements.
We show that simple vibration sensors on wind turbines can reveal how each machine is operating...
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