Sánchez Granados, PabloGómez Muñoz, Carlos QuiterioGarcía Márquez, Fausto Pedro2020-11-102020-11-102020Sánchez, P., Gómez, C., & García, F. (2020). Detection of structural defects in wind turbine blades employing guided waves and machine learning methods. In Developments in Renewable Energies Offshore: Proceedings of the 4th International Conference on Renewable Energies Offshore (pp. 509-514). CRC Press.9781003134572http://hdl.handle.net/11268/9378The evolution of the current energy market towards a more sustainable model justifies a greater presence of renewable energies in the generation of electricity worldwide. Most of this energy is produced by wind turbines, whose blades must be in good structural condition to guarantee the correct performance. This paper employed non-destructive technique based on ultrasound signals to detect faults in wind turbine blades. This article shows the results found by ultrasound guided waves to detect delamination and using Machine Learning techniques. The tests were done to two real wind turbine blades. One blade is free faults while the other blade has delamination induced in the manufacturing process. The novelty of this paper lies in the use of different classifiers to detect delamination in the blades, analysing their performance and determining which of them provides better accuracy.engconference outputrestricted accessAerogeneradoresMantenimiento industrialAprendizaje automáticoEnergía eólicaMantenimientoInteligencia artificial