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.
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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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DOI: https://doi.org/10.1007/978-3-030-87869-6_4
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