Fault Diagnosis of 3D-Printed Scaled Wind Turbine Blades
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908356393828352 |
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| author | Esquivel-Sancho, Luis Miguel Tehrani, Maryam Ghandchi Muñoz-Arias, Mauricio Askari, Mahmoud |
| author_facet | Esquivel-Sancho, Luis Miguel Tehrani, Maryam Ghandchi Muñoz-Arias, Mauricio Askari, Mahmoud |
| contents | This study presents an integrated methodology for fault detection in wind turbine blades using 3D-printed scaled models, finite element simulations, experimental modal analysis, and machine learning techniques. A scaled model of the NREL 5MW blade was fabricated using 3D printing, and crack-type damages were introduced at critical locations. Finite Element Analysis was employed to predict the impact of these damages on the natural frequencies, with the results validated through controlled hammer impact tests. Vibration data was processed to extract both time-domain and frequency-domain features, and key discriminative variables were identified using statistical analyses (ANOVA). Machine learning classifiers, including Support Vector Machine and K-Nearest Neighbors, achieved classification accuracies exceeding 94%. The results revealed that vibration modes 3, 4, and 6 are particularly sensitive to structural anomalies for this blade. This integrated approach confirms the feasibility of combining numerical simulations with experimental validations and paves the way for structural health monitoring systems in wind energy applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06080 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fault Diagnosis of 3D-Printed Scaled Wind Turbine Blades Esquivel-Sancho, Luis Miguel Tehrani, Maryam Ghandchi Muñoz-Arias, Mauricio Askari, Mahmoud Machine Learning This study presents an integrated methodology for fault detection in wind turbine blades using 3D-printed scaled models, finite element simulations, experimental modal analysis, and machine learning techniques. A scaled model of the NREL 5MW blade was fabricated using 3D printing, and crack-type damages were introduced at critical locations. Finite Element Analysis was employed to predict the impact of these damages on the natural frequencies, with the results validated through controlled hammer impact tests. Vibration data was processed to extract both time-domain and frequency-domain features, and key discriminative variables were identified using statistical analyses (ANOVA). Machine learning classifiers, including Support Vector Machine and K-Nearest Neighbors, achieved classification accuracies exceeding 94%. The results revealed that vibration modes 3, 4, and 6 are particularly sensitive to structural anomalies for this blade. This integrated approach confirms the feasibility of combining numerical simulations with experimental validations and paves the way for structural health monitoring systems in wind energy applications. |
| title | Fault Diagnosis of 3D-Printed Scaled Wind Turbine Blades |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.06080 |