{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T22:56:41Z","timestamp":1780354601520,"version":"3.54.1"},"reference-count":69,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,21]],"date-time":"2021-05-21T00:00:00Z","timestamp":1621555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>One of the most critical devices in an electrical system is the transformer. It is continuously under different electrical and mechanical stresses that can produce failures in its components and other electrical network devices. The short-circuited turns (SCTs) are a common winding failure. This type of fault has been widely studied in literature employing the vibration signals produced in the transformer. Although promising results have been obtained, it is not a trivial task if different severity levels and a common high-level noise are considered. This paper presents a methodology based on statistical time features (STFs) and support vector machines (SVM) to diagnose a transformer under several SCTs conditions. As STFs, 19 indicators from the transformer vibration signals are computed; then, the most discriminant features are selected using the Fisher score analysis, and the linear discriminant analysis is used for dimension reduction. Finally, a support vector machine classifier is employed to carry out the diagnosis in an automatic way. Once the methodology has been developed, it is implemented on a field-programmable gate array (FPGA) to provide a system-on-a-chip solution. A modified transformer capable of emulating different SCTs severities is employed to validate and test the methodology and its FPGA implementation. Results demonstrate the effectiveness of the proposal for diagnosing the transformer condition as an accuracy of 96.82% is obtained.<\/jats:p>","DOI":"10.3390\/s21113598","type":"journal-article","created":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:01:20Z","timestamp":1621814480000},"page":"3598","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0669-1178","authenticated-orcid":false,"given":"Jose R.","family":"Huerta-Rosales","sequence":"first","affiliation":[{"name":"ENAP-Research Group, CA-Sistemas Din\u00e1micos y Control, Laboratorio de Sistemas y Equipos El\u00e9ctricos (LaSEE), Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro (UAQ), Campus San Juan del R\u00edo, R\u00edo Moctezuma 249, Col. San Cayetano, San Juan del R\u00edo, CP 76807, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6692-5469","authenticated-orcid":false,"given":"David","family":"Granados-Lieberman","sequence":"additional","affiliation":[{"name":"ENAP-Research Group, CA-Fuentes Alternas y Calidad de la Energ\u00eda El\u00e9ctrica, Departamento de Ingenier\u00eda Electromec\u00e1nica, Tecnol\u00f3gico Nacional de M\u00e9xico, Instituto Tecnol\u00f3gico Superior de Irapuato (ITESI), Carr. Irapuato-Silao km 12.5, Colonia El Copal, Irapuato, Guanajuato, CP 36821, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7070-1855","authenticated-orcid":false,"given":"Arturo","family":"Garcia-Perez","sequence":"additional","affiliation":[{"name":"ENAP-Research Group, Divisi\u00f3n de Ingenier\u00eda, Universidad de Guanajuato, Campus Irapuato-Salamanca, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca, Guanajuato, CP 36885, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Camarena-Martinez","sequence":"additional","affiliation":[{"name":"ENAP-Research Group, Divisi\u00f3n de Ingenier\u00eda, Universidad de Guanajuato, Campus Irapuato-Salamanca, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca, Guanajuato, CP 36885, 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-Research Group, CA-Sistemas Din\u00e1micos y Control, Laboratorio de Sistemas y Equipos El\u00e9ctricos (LaSEE), Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro (UAQ), Campus San Juan del R\u00edo, R\u00edo Moctezuma 249, Col. San Cayetano, San Juan del R\u00edo, CP 76807, Mexico"}],"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-Research Group, CA-Sistemas Din\u00e1micos y Control, Laboratorio de Sistemas y Equipos El\u00e9ctricos (LaSEE), Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro (UAQ), Campus San Juan del R\u00edo, R\u00edo Moctezuma 249, Col. San Cayetano, San Juan del R\u00edo, CP 76807, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.1109\/TDEI.2012.6396941","article-title":"Advanced transformer winding deformation diagnosis: Moving from off-line to on-line","volume":"19","author":"Bagheri","year":"2012","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"112494","DOI":"10.1109\/ACCESS.2019.2932497","article-title":"Classifying Transformer Winding Deformation Fault Types and Degrees Using FRA Based on Support Vector Machine","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hu, Y., Zheng, J., and Huang, H. (2019). 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