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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Sep 2025
Research article |
| 11 Sep 2025
| 11 Sep 2025
Leveraging signal processing and machine learning for automated fault detection in wind turbine drivetrains
Acoustics & Vibration Research Group/OWI-Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Elsene, Belgium
Artificial Intelligence Lab Brussels, Vrije Universiteit Brussel, Pleinlaan 9, 1050 Elsene, Belgium
Cédric Peeters
Acoustics & Vibration Research Group/OWI-Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Elsene, Belgium
Timothy Verstraeten
Acoustics & Vibration Research Group/OWI-Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Elsene, Belgium
Artificial Intelligence Lab Brussels, Vrije Universiteit Brussel, Pleinlaan 9, 1050 Elsene, Belgium
Jan Helsen
Acoustics & Vibration Research Group/OWI-Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Elsene, Belgium
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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
Bai, M., Yang, X., Liu, J., Liu, J., and Yu, D.: Convolutional neural network-based deep transfer learning for fault detection of gas turbine combustion chambers, Appl. Energ., 302, 117509, https://doi.org/10.1016/j.apenergy.2021.117509, 2021. a
Barbini, L., Ompusunggu, A. P., Hillis, A., du Bois, J., and Bartic, A.: Phase editing as a signal pre-processing step for automated bearing fault detection, Mech. Syst. Signal Pr., 91, 407–421, 2017. a
Beretta, M., Julian, A., Sepulveda, J., Cusidó, J., and Porro, O.: An Ensemble Learning Solution for Predictive Maintenance of Wind Turbines Main Bearing, Sensors, 21, 1512, https://doi.org/10.3390/s21041512, 2021. a
Carvalho, T. P., Soares, F. A. A. M. N., Vita, R., da P. Francisco, R., Basto, J. P., and Alcalá, S. G. S.: A systematic literature review of machine learning methods applied to predictive maintenance, Comput. Ind. Eng., 137, 106024, https://doi.org/10.1016/j.cie.2019.106024, 2019. a
Chandola, V., Banerjee, A., and Kumar, V.: Anomaly Detection: A Survey, ACM Comput. Surv., 41, 1–58, https://doi.org/10.1145/1541880.1541882, 2009. a
Chesterman, X., Verstraeten, T., Daems, P., Sanjines, F. P., Nowé, A., and Helsen, J.: The detection of generator bearing failures on wind turbines using machine learning based anomaly detection, J. Phys. Conf. Ser., 2265, 032066, https://doi.org/10.1088/1742-6596/2265/3/032066, 2022. a, b
Chesterman, X., Verstraeten, T., Daems, P.-J., Nowé, A., and Helsen, J.: Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated on data from operational wind farms, Wind Energ. Sci., 8, 893–924, https://doi.org/10.5194/wes-8-893-2023, 2023. a
Clark, C. E. and DuPont, B.: Reliability-based design optimization in offshore renewable energy systems, Renew. Sust. Energ. Rev., 97, 390–400, https://doi.org/10.1016/j.rser.2018.08.030, 2018. a
Dibaj, A., Gao, Z., and Nejad, A. R.: Fault detection of offshore wind turbine drivetrains in different environmental conditions through optimal selection of vibration measurements, Renewable Energy, 203, 161–176, https://doi.org/10.1016/j.renene.2022.12.049, 2023. a
Dienst, S. and Beseler, J.: Automatic anomaly detection in offshore wind SCADA data, Wind Europe Summit, Hamburg, Germany, 27–29 September 2016, https://windeurope.org/summit2016/conference/submit-an-abstract/pdf/626738292593.pdf (last access: 5 September 2025), 2016. a
Gao, Z. and Odgaard, P.: Real-time monitoring, fault prediction and health management for offshore wind turbine systems, Renewable Energy, 218, 119258, https://doi.org/10.1016/j.renene.2023.119258, 2023. a
García Márquez, F. P., Tobias, A. M., Pinar Pérez, J. M., and Papaelias, M.: Condition monitoring of wind turbines: Techniques and methods, Renewable Energy, 46, 169–178, https://doi.org/10.1016/j.renene.2012.03.003, 2012. a
Helsen, J., Devriendt, C., Weijtjens, W., and Guillaume, P.: Condition monitoring by means of scada analysis, in: Proceedings of European wind energy association international conference, Paris, France, 17–20 November 2015, 2015. a
Helsen, J., Peeters, C., Doro, P., Ververs, E., and Jordaens, P. J.: Wind Farm Operation and Maintenance Optimization Using Big Data, in: 2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService), 6–9 April 2017, 179–184, https://doi.org/10.1109/BigDataService.2017.27, 2017. a, b
Helsen, J., Peeters, C., Verstraeten, T., Verbeke, J., Gioia, N., and Nowé, A.: Fleet-wide condition monitoring combining vibration signal processing and machine learning rolled out in a cloud-computing environment, in: International Conference on Noise and Vibration Engineering (ISMA), Leuven, Belgium, 17–19 September 2018, 17–19, https://past.isma-isaac.be/downloads/isma2018/proceedings/Contribution_262_proceeding_3.pdf (last access: 5 September 2025), 2018. a, b
Ho, D.: Bearing diagnostics and self-adaptive noise cancellation, PhD thesis, UNSW Sydney, https://doi.org/10.26190/unsworks/4524, 1999. a
Ho, D. and Randall, R.: Optimisation of Bearing Diagnostic Techniques Using Simulated and Actual Bearing Fault Signals, Mech. Syst. Signal Pr., 14, 763–788, https://doi.org/10.1006/mssp.2000.1304, 2000. a
Hutchinson, M. and Zhao, F.: Global Wind Report 2023, https://gwec.net/globalwindreport2023/ (last access: 26 September 2023), 2023. a
Ibrahim, R., Weinert, J., and Watson, S.: Neural networks for wind turbine fault detection via current signature analysis, Wind Europe summit, Hamburg, Germany, 27–29 September 2016, 2016. a
IRENA and CPI: Global landscape of renewable energy finance 2023, International Renewable Energy Agency, Abu Dhabi, https://www.irena.org/Publications/2023/Feb/Global-landscape-of-renewable-energy-finance-2023, last access: 26 September 2023. a
Jamil, F., Verstraeten, T., Nowé, A., Peeters, C., and Helsen, J.: A deep boosted transfer learning method for wind turbine gearbox fault detection, Renewable Energy, 197, 331–341, https://doi.org/10.1016/j.renene.2022.07.117, 2022. a
Jamil, F., Jara Avila, F., Vratsinis, K., Peeters, C., and Helsen, J.: Wind Turbine Drivetrain Fault Detection Using Multi-Variate Deep Learning Combined With Signal Processing, in: Turbo Expo: Power for Land, Sea, and Air, vol. 87127, V014T37A003, American Society of Mechanical Engineers, https://doi.org/10.1115/GT2023-101689, 2023a. a, b
Jamil, F., Peeters, C., Verstraeten, T., and Helsen, J.: Wind turbine drivetrain fault detection using physics-informed multivariate deep learning, in: Surveillance, Vibrations, Shock and Noise, Institut Supérieur de l'Aéronautique et de l'Espace [ISAE-SUPAERO], 10–13 July 2023, Toulouse, France, https://hal.science/hal-04166103, 2023b. a, b
Jia, F., Lei, Y., Lin, J., Zhou, X., and Lu, N.: Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, Mech. Syst. Signal Pr., 72–73, 303–315, https://doi.org/10.1016/j.ymssp.2015.10.025, 2016. a
Kestel, K., Antoni, J., Peeters, C., Leclère, Q., Girardin, F., Ooijevaar, T., and Helsen, J.: The design of optimal indicators for early fault detection using a generalized likelihood ratio test, in: Surveillance, Vibrations, Shock and Noise, 10–13 July 2023, Toulouse, France, https://hal.science/hal-04165952v1, 2023. a
Leclère, Q., André, H., and Antoni, J.: A multi-order probabilistic approach for Instantaneous Angular Speed tracking debriefing of the CMMNO14' diagnosis contest, Mech. Syst. Signal Pr., 81, 375–386, https://doi.org/10.1016/j.ymssp.2016.02.053, 2016. a
Lima, L. A. M., Blatt, A., and Fujise, J.: Wind Turbine Failure Prediction Using SCADA Data, J. Phys. Conf. Series, 1618, 022017, https://doi.org/10.1088/1742-6596/1618/2/022017, 2020. a
Liu, D., Cui, L., and Cheng, W.: Fault diagnosis of wind turbines under nonstationary conditions based on a novel tacho-less generalized demodulation, Renewable Energy, 206, 645–657, https://doi.org/10.1016/j.renene.2023.01.056, 2023. a
Liu, R., Yang, B., Zio, E., and Chen, X.: Artificial intelligence for fault diagnosis of rotating machinery: A review, Mech. Syst. Signal Pr., 108, 33–47, https://doi.org/10.1016/j.ymssp.2018.02.016, 2018. a
Ma, S., Ding, W., Liu, Y., Ren, S., and Yang, H.: Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries, Appl. Energ., 326, 119986, https://doi.org/10.1016/j.apenergy.2022.119986, 2022. a
Marugán, A. P., Márquez, F. P. G., Perez, J. M. P., and Ruiz-Hernández, D.: A survey of artificial neural network in wind energy systems, Appl. Energ., 228, 1822–1836, https://doi.org/10.1016/j.apenergy.2018.07.084, 2018. a
McCormick, A. and Nandi, A.: Cyclostationarity in Rotating Machine Vibrations, Mech. Syst. Signal Pr., 12, 225–242, https://doi.org/10.1006/mssp.1997.0148, 1998. a
Napolitano, A.: Cyclostationarity: New trends and applications, Signal Process., 120, 385–408, 2016. a
Nejad, A. R., Keller, J., Guo, Y., Sheng, S., Polinder, H., Watson, S., Dong, J., Qin, Z., Ebrahimi, A., Schelenz, R., Gutiérrez Guzmán, F., Cornel, D., Golafshan, R., Jacobs, G., Blockmans, B., Bosmans, J., Pluymers, B., Carroll, J., Koukoura, S., Hart, E., McDonald, A., Natarajan, A., Torsvik, J., Moghadam, F. K., Daems, P.-J., Verstraeten, T., Peeters, C., and Helsen, J.: Wind turbine drivetrains: state-of-the-art technologies and future development trends, Wind Energ. Sci., 7, 387–411, https://doi.org/10.5194/wes-7-387-2022, 2022. a, b, c
Peeters, C., Guillaume, P., and Helsen, J.: A comparison of cepstral editing methods as signal pre-processing techniques for vibration-based bearing fault detection, Mech. Syst. Signal Pr., 91, 354–381, https://doi.org/10.1016/j.ymssp.2016.12.036, 2017. a, b
Peeters, C., Gioia, N., and Helsen, J.: Stochastic simulation assessment of an automated vibration-based condition monitoring framework for wind turbine gearbox faults, J. Phys. Conf. Series, 1037, 032044, https://doi.org/10.1088/1742-6596/1037/3/032044, 2018a. a
Peeters, C., Guillaume, P., and Helsen, J.: Vibration-based bearing fault detection for operations and maintenance cost reduction in wind energy, Renewable Energy, 116, 74–87, https://doi.org/10.1016/j.renene.2017.01.056, 2018b. a
Peeters, C., Leclère, Q., Antoni, J., Lindahl, P., Donnal, J., Leeb, S., and Helsen, J.: Review and comparison of tacholess instantaneous speed estimation methods on experimental vibration data, Mech. Syst. Signal Pr., 129, 407–436, https://doi.org/10.1016/j.ymssp.2019.02.031, 2019a. a
Peeters, C., Verstraeten, T., Nowé, A., and Helsen, J.: Wind turbine planetary gear fault identification using statistical condition indicators and machine learning, in: International conference on offshore mechanics and arctic engineering, vol. 58899, V010T09A014, American Society of Mechanical Engineers, https://doi.org/10.1115/OMAE2019-96713, 2019b. a, b, c, d, e
Peeters, C., Verstraeten, T., Nowé, A., and Helsen, J.: Wind turbine planetary gear fault identification using statistical condition indicators and machine learning, in: International conference on offshore mechanics and arctic engineering, vol. 58899, V010T09A014, American Society of Mechanical Engineers, 2019c. a
Peeters, C., Antoni, J., Daems, P.-J., and Helsen, J.: Separation of vibration signal content using an improved discrete-random separation method, in: Separation of vibration signal content using an improved discrete-random separation method, ISMA 2020, 22–24 September 2020, Leuven, Belgium, https://hal.science/hal-03212000v1, 2020. a
Peeters, C., Antoni, J., Leclère, Q., Verstraeten, T., and Helsen, J.: Multi-harmonic phase demodulation method for instantaneous angular speed estimation using harmonic weighting, Mech. Syst. Signal Pr., 167, 108533, https://doi.org/10.1016/j.ymssp.2021.108533, 2022. a
Perez-Sanjines, F., Peeters, C., Verstraeten, T., Antoni, J., Nowé, A., and Helsen, J.: Fleet-based early fault detection of wind turbine gearboxes using physics-informed deep learning based on cyclic spectral coherence, Mech. Syst. Signal Pr., 185, 109760, https://doi.org/10.1016/j.ymssp.2022.109760, 2023. a, b
Peter, R., Zappalá, D., Schamboeck, V., and Watson, S. J.: Wind turbine generator prognostics using field SCADA data, J. Phys. Conf. Ser., 2265, 032111, https://doi.org/10.1088/1742-6596/2265/3/032111, 2022. a
Randall, R. B.: Vibration-based condition monitoring: industrial, automotive and aerospace applications, John Wiley & Sons, ISBN 9780470747858, https://doi.org/10.1002/9780470977668, 2021. a
Renström, N., Bangalore, P., and Highcock, E.: System-wide anomaly detection in wind turbines using deep autoencoders, Renewable Energy, 157, 647–659, https://doi.org/10.1016/j.renene.2020.04.148, 2020. a
Tautz-Weinert, J. and Watson, S. J.: Using SCADA data for wind turbine condition monitoring–a review, IET Renew. Power Gen., 11, 382–394, https://doi.org/10.1049/iet-rpg.2016.0248, 2017. a
Verstraeten, T., Gomez Marulanda, F., Peeters, C., Daems, P.-J., Nowé, A., and Helsen, J.: Edge computing for advanced vibration signal processing, in: Surveillance, Vishno and AVE conferences, INSA-Lyon, Université de Lyon, 8–10 July 2019, Lyon, France, https://hal.science/hal-02188766, 2019a. a
Verstraeten, T., Nowé, A., Keller, J., Guo, Y., Sheng, S., and Helsen, J.: Fleetwide data-enabled reliability improvement of wind turbines, Renew. Sust. Energ. Rev., 109, 428–437, https://doi.org/10.1016/j.rser.2019.03.019, 2019b. a
Vidal, Y., Pozo, F., and Tutivén, C.: Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data, Energies, 11, 3018, https://doi.org/10.3390/en11113018, 2018. a
Wang, A., Pei, Y., Qian, Z., Zareipour, H., Jing, B., and An, J.: A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification, Appl. Energ., 321, 119373, https://doi.org/10.1016/j.apenergy.2022.119373, 2022a. a
Wang, J., Zhang, X., and Zeng, J.: Dynamic group-maintenance strategy for wind farms based on imperfect maintenance model, Ocean Eng., 259, 111311, https://doi.org/10.1016/j.oceaneng.2022.111311, 2022b. a
Widodo, A. and Yang, B.-S.: Support vector machine in machine condition monitoring and fault diagnosis, Mech. Syst. Signal Pr., 21, 2560–2574, https://doi.org/10.1016/j.ymssp.2006.12.007, 2007. a
Xiang, L., Yang, X., Hu, A., Su, H., and Wang, P.: Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks, Appl. Energ., 305, 117925, https://doi.org/10.1016/j.apenergy.2021.117925, 2022. a
Zhang, M., Cui, H., Li, Q., Liu, J., Wang, K., and Wang, Y.: An improved sideband energy ratio for fault diagnosis of planetary gearboxes, J. Sound Vib., 491, 115712, https://doi.org/10.1016/j.jsv.2020.115712, 2021. a
Zhang, W., Yang, D., and Wang, H.: Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey, IEEE Syst. J., 13, 2213–2227, https://doi.org/10.1109/JSYST.2019.2905565, 2019. a
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., and He, Q.: A Comprehensive Survey on Transfer Learning, P. IEEE, 109, 43–76, https://doi.org/10.1109/JPROC.2020.3004555, 2021. a
Zonta, T., da Costa, C. A., da Rosa Righi, R., de Lima, M. J., da Trindade, E. S., and Li, G. P.: Predictive maintenance in the Industry 4.0: A systematic literature review, Comput. Ind. Eng., 150, 106889, https://doi.org/10.1016/j.cie.2020.106889, 2020. a
Short summary
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.
A hybrid fault detection method is proposed, which combines physical domain knowledge with...
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