EventsThe 6th International Electronic Conference on Applied Sciences
Event submissions
Published
This submission belongs to the session S4. Electrical, Electronics and Communications Engineering of the event The 6th International Electronic Conference on Applied Sciences
Published date
03 Dec, 2025
Academic Editor
author-avatarAlessandro Lo Schiavo
Citation
Jose Luis Garcia Tucci, Jordi Burriel-Valencia, Ángel Sapena-Bañó, Javier Martínez-Román, Kevin Barrera, Non-invasive diagnosis of broken rotor bars in induction motors using deep learning and GASF representations, in Proceedings of The 6th International Electronic Conference on Applied Sciences, 9 December–11 December 2025, MDPI: Basel, Switzerland
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Non-invasive diagnosis of broken rotor bars in induction motors using deep learning and GASF representations

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Jose Luis Garcia Tucci 1
Jordi Burriel-Valencia 2
Ángel Sapena-Bañó 2
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Javier Martínez-Román 2
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Kevin Barrera 1,2,3
1. Universitat Oberta de Catalunya (UOC), Av. del Tibidabo 39-43, 08035, Barcelona, Spain
2. Institute for Energy Engineering, Universitat Politècnica de València, Cmno. de Vera s/n, 46022, Valencia, Spain
3. Control, Data and Artificial Intelligence (CoDAlab), Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Eduard Maristany 16, 08019, Barcelona, Spain
Abstract
Keywords
broken rotor bar
induction motor
fault diagnosis
predictive maintenance
deep learning
FEMM
GASF
ResNet
non-invasive method
Poster
Poster_ASEC2025_BRB_FINAL.pdf
Poster_ASEC2025_BRB_FINAL.pdf