Machine Learning for Wind Turbine Blades Maintenance Management
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,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-12-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/11/1/13 |
| _version_ | 1817991822118486016 |
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| author | Alfredo Arcos Jiménez Carlos Quiterio Gómez Muñoz Fausto Pedro García Márquez |
| author_facet | Alfredo Arcos Jiménez Carlos Quiterio Gómez Muñoz Fausto Pedro García Márquez |
| author_sort | Alfredo Arcos Jiménez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T01:18:51Z |
| format | Article |
| id | doaj.art-1236f9706579411ba6003c9b86fc7615 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T01:18:51Z |
| publishDate | 2017-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-1236f9706579411ba6003c9b86fc76152022-12-22T02:20:44ZengMDPI AGEnergies1996-10732017-12-011111310.3390/en11010013en11010013Machine Learning for Wind Turbine Blades Maintenance ManagementAlfredo Arcos Jiménez0Carlos Quiterio Gómez Muñoz1Fausto Pedro García Márquez2Ingenium Research Group, Castilla-La Mancha University, 13071 Ciudad Real, SpainIngeniería Industrial y Aeroespacial, Universidad Europea Madrid, Villaviciosa de Odón, 28670 Madrid, SpainIngenium Research Group, Castilla-La Mancha University, 13071 Ciudad Real, SpainDelamination 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.https://www.mdpi.com/1996-1073/11/1/13delamination detectionmacro fiber compositewavelet transformsnon-destructive testsneural networkguided waveswind turbine blade |
| spellingShingle | Alfredo Arcos Jiménez Carlos Quiterio Gómez Muñoz Fausto Pedro García Márquez Machine Learning for Wind Turbine Blades Maintenance Management Energies delamination detection macro fiber composite wavelet transforms non-destructive tests neural network guided waves wind turbine blade |
| title | Machine Learning for Wind Turbine Blades Maintenance Management |
| title_full | Machine Learning for Wind Turbine Blades Maintenance Management |
| title_fullStr | Machine Learning for Wind Turbine Blades Maintenance Management |
| title_full_unstemmed | Machine Learning for Wind Turbine Blades Maintenance Management |
| title_short | Machine Learning for Wind Turbine Blades Maintenance Management |
| title_sort | machine learning for wind turbine blades maintenance management |
| topic | delamination detection macro fiber composite wavelet transforms non-destructive tests neural network guided waves wind turbine blade |
| url | https://www.mdpi.com/1996-1073/11/1/13 |
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