Machine Learning for Wind Turbine Blades Maintenance Management

Alfredo Arcos Jiménez, Carlos Quiterio Gómez Muñoz and Fausto Pedro García Márquez
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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

Energies, 2017, vol. 11, issue 1, 1-16

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.

Keywords: delamination detection; macro fiber composite; wavelet transforms; non-destructive tests; neural network; guided waves; wind turbine blade (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
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