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

José Figueroa; José M. Saavedra; José F. Delpiano; Rodrigo Astroza

doi:10.1007/978-3-031-39117-0_47

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 congreso › Contribución a la conferencia › revisión exhaustiva

1 Cita (Scopus)

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 original	Inglés
Título de la publicación alojada	Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023
Editores	Maria Pina Limongelli, Pier Francesco Giordano, Carmelo Gentile, Said Quqa, Alfredo Cigada
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	458-467
Número de páginas	10
Volumen	2
ISBN (versión impresa)	9783031391163
DOI	https://doi.org/10.1007/978-3-031-39117-0_47
Estado	Publicada - 2023
Evento	Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2 - Milan, Italia Duración: 30 ago. 2023 → 1 sep. 2023

Serie de la publicación

Nombre	Lecture Notes in Civil Engineering
Volumen	433 LNCE
ISSN (versión impresa)	2366-2557
ISSN (versión digital)	2366-2565

Conferencia

Conferencia	Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2
País/Territorio	Italia
Ciudad	Milan
Período	30/08/23 → 1/09/23

Nota bibliográfica

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

Acceder al documento

10.1007/978-3-031-39117-0_47

Otros archivos y enlaces

Enlace a la publicación en Scopus

Citar esto

Figueroa, J., Saavedra, J. M., Delpiano, J. F., & Astroza, R. (2023). Structural Damage Diagnosis of Wind Turbine Blades Based on Machine Learning Techniques. En M. P. Limongelli, P. F. Giordano, C. Gentile, S. Quqa, & A. Cigada (Eds.), Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 (Vol. 2, pp. 458-467). (Lecture Notes in Civil Engineering; Vol. 433 LNCE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-39117-0_47

Figueroa, José ; Saavedra, José M. ; Delpiano, José F. et al. / Structural Damage Diagnosis of Wind Turbine Blades Based on Machine Learning Techniques. Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023. editor / Maria Pina Limongelli ; Pier Francesco Giordano ; Carmelo Gentile ; Said Quqa ; Alfredo Cigada. Vol. 2 Springer Science and Business Media Deutschland GmbH, 2023. pp. 458-467 (Lecture Notes in Civil Engineering).

@inproceedings{d6997e650df74262926e68d33ffb2248,

title = "Structural Damage Diagnosis of Wind Turbine Blades Based on Machine Learning Techniques",

abstract = "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.",

keywords = "Artificial Neural Network, Damage Diagnosis, Structural Health Monitoring, Wind Turbine",

author = "Jos{\'e} Figueroa and Saavedra, {Jos{\'e} M.} and Delpiano, {Jos{\'e} F.} and Rodrigo Astroza",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2 ; Conference date: 30-08-2023 Through 01-09-2023",

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Figueroa, J, Saavedra, JM , Delpiano, JF & Astroza, R 2023, Structural Damage Diagnosis of Wind Turbine Blades Based on Machine Learning Techniques. En MP Limongelli, PF Giordano, C Gentile, S Quqa & A Cigada (eds.), Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023. vol. 2, Lecture Notes in Civil Engineering, vol. 433 LNCE, Springer Science and Business Media Deutschland GmbH, pp. 458-467, Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2, Milan, Italia, 30/08/23. https://doi.org/10.1007/978-3-031-39117-0_47

Structural Damage Diagnosis of Wind Turbine Blades Based on Machine Learning Techniques. / Figueroa, José; Saavedra, José M.; Delpiano, José F. et al.
Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023. ed. / Maria Pina Limongelli; Pier Francesco Giordano; Carmelo Gentile; Said Quqa; Alfredo Cigada. Vol. 2 Springer Science and Business Media Deutschland GmbH, 2023. p. 458-467 (Lecture Notes in Civil Engineering; Vol. 433 LNCE).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - Structural Damage Diagnosis of Wind Turbine Blades Based on Machine Learning Techniques

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AB - 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.

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KW - Structural Health Monitoring

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Figueroa J, Saavedra JM , Delpiano JF , Astroza R. Structural Damage Diagnosis of Wind Turbine Blades Based on Machine Learning Techniques. En Limongelli MP, Giordano PF, Gentile C, Quqa S, Cigada A, editores, Experimental Vibration Analysis for Civil Engineering Structures - EVACES 2023. Vol. 2. Springer Science and Business Media Deutschland GmbH. 2023. p. 458-467. (Lecture Notes in Civil Engineering). doi: 10.1007/978-3-031-39117-0_47