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Group of Safety, Reliability and Maintenance > 5.3 System Reliability and Availability > Publications
Sobral, J., Datia, N., Pato, M. and Guedes Soares, C. (2025), Machine learning algorithms to use in the development of maintenance strategies for wind turbines, Innovations in Renewable Energies Offshore, Guedes Soares, C. & Wang S. (Ed.), Taylor and Francis Group, London, UK, 491-501.
Machine learning algorithms are commonly employed to analyze vast amounts of data collected by condition monitoring systems in wind turbines to identify patterns, anomalies, and trends in the data, ena-bling predictive maintenance and decision-making. This paper addresses several possibilities and potential uses of these algorithms, describing some proposals and inherent objectives, and presenting possible outcomes that can play a crucial role in developing predictive maintenance strategies to enhance the reliability and efficiency of wind energy systems. The paper also presents a case study, showing how to deal with data from a Supervisory Control and Data Acquisition system and use it in machine learning algorithms.
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