{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T13:44:44Z","timestamp":1779889484025,"version":"3.53.1"},"reference-count":24,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T00:00:00Z","timestamp":1757116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"\u201cFondo para el Fortalecimiento de la Investigaci\u00f3n, Vinculaci\u00f3n y Extensi\u00f3n (FONFIVE-UAQ 2025)\u201d project"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The detection of damage in wind turbine blades is critical for ensuring their operational efficiency and longevity. This study presents a novel method for wind turbine blade damage detection, utilizing Gramian Angular Field (GAF) transformations of vibration signals in combination with Convolutional Neural Networks (CNNs). The GAF method enables the transformation of vibration signals, which are captured using a triaxial accelerometer, into angular representations that preserve temporal dependencies and reveal distinctive texture patterns that can be associated with structural damage. This transformation facilitates the capability of CNNs to identify complex features correlated with crack severity in wind turbine blades, thereby enhancing the precision and effectiveness of turbine fault diagnosis. The GAF-CNN model achieved a notable classification accuracy over 99.9%, demonstrating its robustness and potential for automated damage detection. Unlike traditional methods, which rely on expert interpretation and are sensitive to noise, the proposed system offers a more efficient and precise tool for damage monitoring. The findings suggest that this method can significantly enhance wind turbine condition monitoring systems, offering reduced dependency on manual inspections and improving early detection capabilities.<\/jats:p>","DOI":"10.3390\/info16090775","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T09:32:01Z","timestamp":1757496721000},"page":"775","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Gramian Angular Field-Based Convolutional Neural Network Approach for Crack Detection in Low-Power Turbines from Vibration Signals"],"prefix":"10.3390","volume":"16","author":[{"given":"Angel H.","family":"Rangel-Rodriguez","sequence":"first","affiliation":[{"name":"ENAP-RG, CA-Sistemas Din\u00e1micos y Control, Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro, Campus San Juan del R\u00edo, San Juan del R\u00edo 76806, Quer\u00e9taro, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9559-0220","authenticated-orcid":false,"given":"Juan P.","family":"Amezquita-Sanchez","sequence":"additional","affiliation":[{"name":"ENAP-RG, CA-Sistemas Din\u00e1micos y Control, Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro, Campus San Juan del R\u00edo, San Juan del R\u00edo 76806, Quer\u00e9taro, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Granados-Lieberman","sequence":"additional","affiliation":[{"name":"ENAP-RG, Departamento de Ingenier\u00eda Electromec\u00e1nica, Tecnol\u00f3gico Nacional de M\u00e9xico, Instituto Tecnol\u00f3gico Superior de Irapuato, Irapuato 36821, Guanajuato, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0862-0821","authenticated-orcid":false,"given":"David","family":"Camarena-Martinez","sequence":"additional","affiliation":[{"name":"ENAP-RG, Divisi\u00f3n de Ingenier\u00edas, Universidad de Guanajuato, Campus Irapuato-Salamanca, Salamanca 36885, Guanajuato, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximiliano","family":"Bueno-Lopez","sequence":"additional","affiliation":[{"name":"Programa de Tecnolog\u00eda El\u00e9ctrica, Universidad Tecnol\u00f3gica de Pereira, Pereira 660003, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3839-1396","authenticated-orcid":false,"given":"Martin","family":"Valtierra-Rodriguez","sequence":"additional","affiliation":[{"name":"ENAP-RG, CA-Sistemas Din\u00e1micos y Control, Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro, Campus San Juan del R\u00edo, San Juan del R\u00edo 76806, Quer\u00e9taro, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mishnaevsky, L. 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