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

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationExperimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2
EditorsMaria Pina Limongelli, Pier Francesco Giordano, Carmelo Gentile, Said Quqa, Alfredo Cigada
PublisherSpringer Science and Business Media Deutschland GmbH
Pages458-467
Number of pages10
ISBN (Print)9783031391163
DOIs
StatePublished - 2023
EventExperimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2 - Milan, Italy
Duration: 30 Aug 20231 Sep 2023

Publication series

NameLecture Notes in Civil Engineering
Volume433 LNCE
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Conference

ConferenceExperimental Vibration Analysis for Civil Engineering Structures - EVACES 2023 - Volume 2
Country/TerritoryItaly
CityMilan
Period30/08/231/09/23

Bibliographical note

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

Keywords

  • Artificial Neural Network
  • Damage Diagnosis
  • Structural Health Monitoring
  • Wind Turbine

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