Abstract
Predictive maintenance is a key point to reduce cost in energy production. In this work we focus on wind energy and so on wind turbines. We start from the basis of having a sensors-based condition monitoring system installed in the wind turbine, which is in charge of registering measures/signals about some critical components. In this paper we propose a data science-based predictive process which combines two predictive tasks, classification and numerical prediction, to estimate the useful life remaining for those critical components, which are prone to fail. The process is illustrated and evaluated by using real data coming for the R+D MAS4WIN project.
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https://www.grupovermon.com/2019/06/11/aprobacion-del-proyecto-mas4win/ (funded by the Spanish Center for Technological and Industrial Development (CDTI) and FEDER) with code IDI-20190333.
References
Abdallah, I., et al.: Fault diagnosis of wind turbine structures using decision tree learning algorithms with big data. In: Proceedings of the European Safety and Reliability Conference, pp. 3053–3061 (2018)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. CRC Press (1984)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
Friedman. J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)
Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Appl. 13(4), 18–28 (1998)
Heimes, F.O.: Recurrent neural networks for remaining useful life estimation. In: 2008 International Conference on Prognostics and Health Management, pp. 1–6. IEEE (2008)
Kleinbaum, D.G., Klein, M.: Logistic Regression. Springer, Heidelberg (2002). https://doi.org/10.1007/978-1-4419-1742-3
Leahy, K., Hu, R.L., Konstantakopoulos, I.C., Spanos, C.J., Agogino, A.M.: Diagnosing wind turbine faults using machine learning techniques applied to operational data. In: 2016 IEEE International Conference on Prognostics and Health Management (ICPHM) (2016)
Lei, J., Liu, C., Jiang, D.: Fault diagnosis of wind turbine based on Long Short-term memory networks. Renew. Energy 133, 422–432 (2019)
Nuhic, A., Terzimehic, T., Soczka-Guth, T., Buchholz, M., Dietmayer, K.: Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods. J. Power Sources 239, 680–688 (2013)
Si, X.-S., Wang, W., Chang-Hua, H., Zhou, D.-H.: Remaining useful life estimation-a review on the statistical data driven approaches. Eur. J. Oper. Res. 213(1), 1–14 (2011)
Stetco, A., et al.: Machine learning methods for wind turbine condition monitoring: a review. Renew. Energy 133, 620–635 (2019)
von Birgelen, A., Buratti, D., Mager, J., Niggemann. O.: Self-organizing maps for anomaly localization and predictive maintenance in cyber-physical production systems. Procedia CIRP 72, 480–485 (2018). 51st CIRP Conference on Manufacturing Systems
Acknowledgments
Jose A. Gámez has been partially funded by JCCM and FEDER under project SBPLY/17/180501/000493. We thank all MAS4WIN project partners for their support, and specially INGETEAM S.A. for the access to the data.
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Martínez, A.Z., Martínez-Gómez, J., Gámez, J.A. (2022). A Data-Driven Approach for Components Useful Life Estimation in Wind Turbines. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_4
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