Structural Damage Diagnosis of Wind Turbine Blades Based on Machine Learning Techniques

José Figueroa, José M. Saavedra, José F. Delpiano, Rodrigo Astroza*

*Autor correspondiente de este trabajo

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

This paper presents a method for damage identification of wind turbine blades based on vibration data and machine learning (ML) techniques and their validation using experimental data collected at different states of artificially-induced damage. The acceleration responses collected from accelerometers placed along the blades are preprocessed according to the type of network used for damage diagnosis. The ML approach is a supervised strategy in which a multilayered perceptron (MLP) takes a vector of damage-sensitive features, calculated from the acceleration time series. The accuracy of the approach is evaluated, and the effects of the operational and environmental variables (EOV) are discussed.

Idioma originalInglés
Título de la publicación alojadaExperimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2
EditoresMaria Pina Limongelli, Pier Francesco Giordano, Carmelo Gentile, Said Quqa, Alfredo Cigada
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas458-467
Número de páginas10
ISBN (versión impresa)9783031391163
DOI
EstadoPublicada - 2023
EventoExperimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2 - Milan, Italia
Duración: 30 ago. 20231 sep. 2023

Serie de la publicación

NombreLecture Notes in Civil Engineering
Volumen433 LNCE
ISSN (versión impresa)2366-2557
ISSN (versión digital)2366-2565

Conferencia

ConferenciaExperimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2
País/TerritorioItalia
CiudadMilan
Período30/08/231/09/23

Nota bibliográfica

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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