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Machine Learning for Wind Turbine Blades Maintenance Management

Author

Listed:
  • Alfredo Arcos Jiménez

    (Ingenium Research Group, Castilla-La Mancha University, 13071 Ciudad Real, Spain)

  • Carlos Quiterio Gómez Muñoz

    (Ingeniería Industrial y Aeroespacial, Universidad Europea Madrid, Villaviciosa de Odón, 28670 Madrid, Spain)

  • Fausto Pedro García Márquez

    (Ingenium Research Group, Castilla-La Mancha University, 13071 Ciudad Real, Spain)

Abstract

Delamination in Wind Turbine Blades (WTB) is a common structural problem that can generate large costs. Delamination is the separation of layers of a composite material, which produces points of stress concentration. These points suffer greater traction and compression forces in working conditions, and they can trigger cracks, and partial or total breakage of the blade. Early detection of delamination is crucial for the prevention of breakages and downtime. The main novelty presented in this paper has been to apply an approach for detecting and diagnosing the delamination WTB. The approach is based on signal processing of guided waves, and multiclass pattern recognition using machine learning. Delamination was induced in the WTB to check the accuracy of the approach. The signal is denoised by wavelet transform. The autoregressive Yule–Walker model is employed for feature extraction, and Akaike’s information criterion method for feature selection. The classifiers are quadratic discriminant analysis, k-nearest neighbors, decision trees, and neural network multilayer perceptron. The confusion matrix is employed to evaluate the classification, especially the receiver operating characteristic analysis by: recall, specificity, precision, and F -score.

Suggested Citation

  • Alfredo Arcos Jiménez & Carlos Quiterio Gómez Muñoz & Fausto Pedro García Márquez, 2017. "Machine Learning for Wind Turbine Blades Maintenance Management," Energies, MDPI, vol. 11(1), pages 1-16, December.
  • Handle: RePEc:gam:jeners:v:11:y:2017:i:1:p:13-:d:123871
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    References listed on IDEAS

    as
    1. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    2. Fausto Pedro García Márquez & Alberto Pliego Marugán & Jesús María Pinar Pérez & Stuart Hillmansen & Mayorkinos Papaelias, 2017. "Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines," Energies, MDPI, vol. 10(8), pages 1-19, July.
    3. Márquez, Fausto Pedro García & Pérez, Jesús María Pinar & Marugán, Alberto Pliego & Papaelias, Mayorkinos, 2016. "Identification of critical components of wind turbines using FTA over the time," Renewable Energy, Elsevier, vol. 87(P2), pages 869-883.
    4. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 21(1), pages 243-247, December.
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