Version 1
: Received: 19 June 2024 / Approved: 20 June 2024 / Online: 20 June 2024 (15:19:22 CEST)
How to cite:
Araujo, V. G. D.; Bissiriou, A. O.-S.; Villanueva, J. M. M.; Villarreal, E. R. L.; Salazar, A. O.; Texeira, R. D. A.; Fonsêca, D. A. D. M. Monitoring and Diagnosing Faults in Three-Phase Induction Motors Using Artificial Intelligence Techniques. Preprints2024, 2024061451. https://doi.org/10.20944/preprints202406.1451.v1
Araujo, V. G. D.; Bissiriou, A. O.-S.; Villanueva, J. M. M.; Villarreal, E. R. L.; Salazar, A. O.; Texeira, R. D. A.; Fonsêca, D. A. D. M. Monitoring and Diagnosing Faults in Three-Phase Induction Motors Using Artificial Intelligence Techniques. Preprints 2024, 2024061451. https://doi.org/10.20944/preprints202406.1451.v1
Araujo, V. G. D.; Bissiriou, A. O.-S.; Villanueva, J. M. M.; Villarreal, E. R. L.; Salazar, A. O.; Texeira, R. D. A.; Fonsêca, D. A. D. M. Monitoring and Diagnosing Faults in Three-Phase Induction Motors Using Artificial Intelligence Techniques. Preprints2024, 2024061451. https://doi.org/10.20944/preprints202406.1451.v1
APA Style
Araujo, V. G. D., Bissiriou, A. O. S., Villanueva, J. M. M., Villarreal, E. R. L., Salazar, A. O., Texeira, R. D. A., & Fonsêca, D. A. D. M. (2024). Monitoring and Diagnosing Faults in Three-Phase Induction Motors Using Artificial Intelligence Techniques. Preprints. https://doi.org/10.20944/preprints202406.1451.v1
Chicago/Turabian Style
Araujo, V. G. D., Rodrigo de Andrade Texeira and Diego Antonio de Moura Fonsêca. 2024 "Monitoring and Diagnosing Faults in Three-Phase Induction Motors Using Artificial Intelligence Techniques" Preprints. https://doi.org/10.20944/preprints202406.1451.v1
Abstract
Three-phase induction motors play a key role in industrial operations. However, their failure can result in serious operational problems. This study focuses on the early identification of faults through accurate diagnosis and classification of faults in three-phase induction motors, using artificial intelligence techniques, by analyzing current, temperature, and vibration signals. Experiments were conducted on a test bench, simulating real operating conditions, including stator phase unbalance, bearing damage and shaft unbalance. To classify the faults, an Auto-Regressive Neural Network with Exogenous Inputs (NARX) was developed. The parameters of this network were determined through a process of selecting the best network, using the scanning method with multiple training and validation iterations, with the introduction of new data. The results of these tests showed that the network had excellent generalization in all the situations evaluated, achieving the following accuracy rates: Motor without fault = 94.2%, Unbalance fault = 95%, Bearings with fault =98% and Stator with fault = 95%.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.