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Computation
Volume 13
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10.3390/computation13060140
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Article

Early Detection of Inter-Turn Short Circuits in Induction Motors Using the Derivative of Stator Current and a Lightweight 1D-ResNet

by
Carlos Javier Morales-Perez
1,
David Camarena-Martinez
2,
Juan Pablo Amezquita-Sanchez
1,
Jose de Jesus Rangel-Magdaleno
3,
Edwards Ernesto Sánchez Ramírez
4 and
Martin Valtierra-Rodriguez
1,*
1
ENAP-Research Group, CA-Sistemas Dinámicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, San Juan del Río 76807, Queretaro, Mexico
2
ENAP-Research Group, División de Ingeniería, 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
3
Digital Systems Group, Coordinación de Electrónica, Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Luis Enrique Erro #1, Sta. María Tonanzintla, San Andrés Cholula 72840, Puebla, Mexico
4
Laboratorio de Procesamiento de Imagenes y Señales, ESIME Zacatenco, Instituto Politécnico Nacional (IPN), Unidad Profesional Adolfo López Mateos, Avenida Luis Enrique Erro S/N, UPALM, Alcaldía Gustavo A. Madero 07738, Mexico City, Mexico
*
Author to whom correspondence should be addressed.
Computation 2025, 13(6), 140; https://doi.org/10.3390/computation13060140
Submission received: 13 May 2025 / Revised: 30 May 2025 / Accepted: 2 June 2025 / Published: 4 June 2025
(This article belongs to the Special Issue Diagnosing Faults with Machine Learning)
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Abstract

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’s suitability for real-time fault detection in embedded and resource-constrained industrial environments.
Keywords: current stator signal; incipient fault; induction motor; inter-turn short circuit; residual neural networks; signal derivative current stator signal; incipient fault; induction motor; inter-turn short circuit; residual neural networks; signal derivative

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MDPI and ACS Style

Morales-Perez, C.J.; Camarena-Martinez, D.; Amezquita-Sanchez, J.P.; Rangel-Magdaleno, J.d.J.; Ramírez, E.E.S.; Valtierra-Rodriguez, M. Early Detection of Inter-Turn Short Circuits in Induction Motors Using the Derivative of Stator Current and a Lightweight 1D-ResNet. Computation 2025, 13, 140. https://doi.org/10.3390/computation13060140

AMA Style

Morales-Perez CJ, Camarena-Martinez D, Amezquita-Sanchez JP, Rangel-Magdaleno JdJ, Ramírez EES, Valtierra-Rodriguez M. Early Detection of Inter-Turn Short Circuits in Induction Motors Using the Derivative of Stator Current and a Lightweight 1D-ResNet. Computation. 2025; 13(6):140. https://doi.org/10.3390/computation13060140

Chicago/Turabian Style

Morales-Perez, Carlos Javier, David Camarena-Martinez, Juan Pablo Amezquita-Sanchez, Jose de Jesus Rangel-Magdaleno, Edwards Ernesto Sánchez Ramírez, and Martin Valtierra-Rodriguez. 2025. "Early Detection of Inter-Turn Short Circuits in Induction Motors Using the Derivative of Stator Current and a Lightweight 1D-ResNet" Computation 13, no. 6: 140. https://doi.org/10.3390/computation13060140

APA Style

Morales-Perez, C. J., Camarena-Martinez, D., Amezquita-Sanchez, J. P., Rangel-Magdaleno, J. d. J., Ramírez, E. E. S., & Valtierra-Rodriguez, M. (2025). Early Detection of Inter-Turn Short Circuits in Induction Motors Using the Derivative of Stator Current and a Lightweight 1D-ResNet. Computation, 13(6), 140. https://doi.org/10.3390/computation13060140

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