{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T02:55:24Z","timestamp":1771988124096,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T00:00:00Z","timestamp":1748995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>This work presents a lightweight and practical methodology for detecting inter-turn short-circuit faults in squirrel-cage induction motors under different mechanical load conditions. The proposed approach utilizes a one-dimensional convolutional neural network (1D-CNN) enhanced with residual blocks and trained on differentiated stator current signals obtained under different load mechanical conditions. This preprocessing step enhances fault-related features, enabling improved learning while maintaining the simplicity of a lightweight CNN. The model achieved classification accuracies above 99.16% across all folds in five-fold cross-validation and demonstrated the ability to detect faults involving as few as three short-circuited turns. Comparative experiments with the Multi-Scale 1D-ResNet demonstrate that the proposed method achieves similar or superior performance while significantly reducing training time. These results highlight the model\u2019s suitability for real-time fault detection in embedded and resource-constrained industrial environments.<\/jats:p>","DOI":"10.3390\/computation13060140","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T10:10:16Z","timestamp":1749031816000},"page":"140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Early Detection of Inter-Turn Short Circuits in Induction Motors Using the Derivative of Stator Current and a Lightweight 1D-ResNet"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2378-7021","authenticated-orcid":false,"given":"Carlos Javier","family":"Morales-Perez","sequence":"first","affiliation":[{"name":"ENAP-Research Group, CA-Sistemas Din\u00e1micos, 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 76807, Queretaro, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0862-0821","authenticated-orcid":false,"given":"David","family":"Camarena-Martinez","sequence":"additional","affiliation":[{"name":"ENAP-Research Group, Divisi\u00f3n de Ingenier\u00eda, Universidad de Guanajuato (UG), Campus Irapuato-Salamanca, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Guanajuato, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9559-0220","authenticated-orcid":false,"given":"Juan Pablo","family":"Amezquita-Sanchez","sequence":"additional","affiliation":[{"name":"ENAP-Research Group, CA-Sistemas Din\u00e1micos, 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 76807, Queretaro, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2785-5060","authenticated-orcid":false,"given":"Jose de Jesus","family":"Rangel-Magdaleno","sequence":"additional","affiliation":[{"name":"Digital Systems Group, Coordinaci\u00f3n de Electr\u00f3nica, Instituto Nacional de Astrof\u00edsica, \u00d3ptica y Electr\u00f3nica (INAOE), Luis Enrique Erro #1, Sta. Mar\u00eda Tonanzintla, San Andr\u00e9s Cholula 72840, Puebla, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2653-3214","authenticated-orcid":false,"given":"Edwards Ernesto S\u00e1nchez","family":"Ram\u00edrez","sequence":"additional","affiliation":[{"name":"Laboratorio de Procesamiento de Imagenes y Se\u00f1ales, ESIME Zacatenco, Instituto Polit\u00e9cnico Nacional (IPN), Unidad Profesional Adolfo L\u00f3pez Mateos, Avenida Luis Enrique Erro S\/N, UPALM, Alcald\u00eda Gustavo A. Madero 07738, Mexico City, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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, 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 76807, Queretaro, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1546","DOI":"10.1007\/s11668-022-01445-2","article-title":"A Review to Diagnose Faults Related to Three-Phase Industrial Induction Motors","volume":"22","author":"Sheikh","year":"2022","journal-title":"J. Fail. Anal. Prev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1109\/TIA.2021.3131296","article-title":"A Comparative Investigation of Interturn Faults in Induction Motors Suggesting a Novel Transient Diagnostic Method Based on the Goerges Phenomenon","volume":"58","author":"Gyftakis","year":"2022","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Adouni, A., and Cardoso, A.J.M. (2021). 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