Machine Learning for Wind Turbine Blades Maintenance Management
AbstractDelamination 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. View Full-Text
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Arcos Jiménez, A.; Gómez Muñoz, C.Q.; García Márquez, F.P. Machine Learning for Wind Turbine Blades Maintenance Management. Energies 2018, 11, 13.
Arcos Jiménez A, Gómez Muñoz CQ, García Márquez FP. Machine Learning for Wind Turbine Blades Maintenance Management. Energies. 2018; 11(1):13.Chicago/Turabian Style
Arcos Jiménez, Alfredo; Gómez Muñoz, Carlos Q.; García Márquez, Fausto P. 2018. "Machine Learning for Wind Turbine Blades Maintenance Management." Energies 11, no. 1: 13.
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