Crack Location in Wind Turbine Blades Using Vibration Signal and Support Vector Machine
This study introduces a new method to locate cracks in wind turbine blades using the support vector machine algorithm and the tangential vibration signal measured at the root blade in static conditions. The method was implemented in hardware and experimentally validated on 200 W wind turbine blades....
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Vibration |
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| Online Access: | https://www.mdpi.com/2571-631X/8/2/20 |
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| author | Perla Y. Sevilla-Camacho José B. Robles-Ocampo Juvenal Rodríguez-Resendíz Sergio De la Cruz-Arreola Marco A. Zuñiga-Reyes Edwin N. Hernández-Estrada |
| author_facet | Perla Y. Sevilla-Camacho José B. Robles-Ocampo Juvenal Rodríguez-Resendíz Sergio De la Cruz-Arreola Marco A. Zuñiga-Reyes Edwin N. Hernández-Estrada |
| author_sort | Perla Y. Sevilla-Camacho |
| collection | DOAJ |
| description | This study introduces a new method to locate cracks in wind turbine blades using the support vector machine algorithm and the tangential vibration signal measured at the root blade in static conditions. The method was implemented in hardware and experimentally validated on 200 W wind turbine blades. The blade conditions were healthy, and transverse cracked at the root, midsection, and tip. The experimental procedure is easy, and only one low-cost piezoelectric accelerometer is needed, which is affordable and straightforward to install. The machine learning technique used requires a small dataset and low computing power. The results show exceptional performance, achieving an accuracy of 99.37% and a precision of 98.77%. This approach enhances the reliability of wind turbine blade monitoring. It provides a robust early detection and maintenance solution, improving operational efficiency and safety in wind energy production. K-nearest neighbors and decision trees are also used for comparison purposes. |
| format | Article |
| id | doaj-art-afa68f18ff7b4a7f9c115b488f29f43a |
| institution | OA Journals |
| issn | 2571-631X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Vibration |
| spelling | doaj-art-afa68f18ff7b4a7f9c115b488f29f43a2025-08-20T02:21:52ZengMDPI AGVibration2571-631X2025-04-01822010.3390/vibration8020020Crack Location in Wind Turbine Blades Using Vibration Signal and Support Vector MachinePerla Y. Sevilla-Camacho0José B. Robles-Ocampo1Juvenal Rodríguez-Resendíz2Sergio De la Cruz-Arreola3Marco A. Zuñiga-Reyes4Edwin N. Hernández-Estrada5Cuerpo Académico de Energía y Sustentabilidad, Universidad Politécnica de Chiapas, Carretera Tuxtla Gutiérrez—Portillo Zaragoza Km 21+500, Col. Las Brisas, Suchiapa C.P. 29150, MexicoCuerpo Académico de Energía y Sustentabilidad, Universidad Politécnica de Chiapas, Carretera Tuxtla Gutiérrez—Portillo Zaragoza Km 21+500, Col. Las Brisas, Suchiapa C.P. 29150, MexicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas, Las Campanas, Querétaro C.P. 76010, MexicoCuerpo Académico de Energía y Sustentabilidad, Universidad Politécnica de Chiapas, Carretera Tuxtla Gutiérrez—Portillo Zaragoza Km 21+500, Col. Las Brisas, Suchiapa C.P. 29150, MexicoDepartamento de Metal Mecánica, Tecnológico Nacional de México/IT de Tuxtla Gutiérrez, Carretera Panamericana Km 1080, Tuxtla Gutiérrez C.P. 29050, MexicoCuerpo Académico de Energía y Sustentabilidad, Universidad Politécnica de Chiapas, Carretera Tuxtla Gutiérrez—Portillo Zaragoza Km 21+500, Col. Las Brisas, Suchiapa C.P. 29150, MexicoThis study introduces a new method to locate cracks in wind turbine blades using the support vector machine algorithm and the tangential vibration signal measured at the root blade in static conditions. The method was implemented in hardware and experimentally validated on 200 W wind turbine blades. The blade conditions were healthy, and transverse cracked at the root, midsection, and tip. The experimental procedure is easy, and only one low-cost piezoelectric accelerometer is needed, which is affordable and straightforward to install. The machine learning technique used requires a small dataset and low computing power. The results show exceptional performance, achieving an accuracy of 99.37% and a precision of 98.77%. This approach enhances the reliability of wind turbine blade monitoring. It provides a robust early detection and maintenance solution, improving operational efficiency and safety in wind energy production. K-nearest neighbors and decision trees are also used for comparison purposes.https://www.mdpi.com/2571-631X/8/2/20crack locationwind turbinebladevibration signalmachine learningsupport vector machine |
| spellingShingle | Perla Y. Sevilla-Camacho José B. Robles-Ocampo Juvenal Rodríguez-Resendíz Sergio De la Cruz-Arreola Marco A. Zuñiga-Reyes Edwin N. Hernández-Estrada Crack Location in Wind Turbine Blades Using Vibration Signal and Support Vector Machine Vibration crack location wind turbine blade vibration signal machine learning support vector machine |
| title | Crack Location in Wind Turbine Blades Using Vibration Signal and Support Vector Machine |
| title_full | Crack Location in Wind Turbine Blades Using Vibration Signal and Support Vector Machine |
| title_fullStr | Crack Location in Wind Turbine Blades Using Vibration Signal and Support Vector Machine |
| title_full_unstemmed | Crack Location in Wind Turbine Blades Using Vibration Signal and Support Vector Machine |
| title_short | Crack Location in Wind Turbine Blades Using Vibration Signal and Support Vector Machine |
| title_sort | crack location in wind turbine blades using vibration signal and support vector machine |
| topic | crack location wind turbine blade vibration signal machine learning support vector machine |
| url | https://www.mdpi.com/2571-631X/8/2/20 |
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