Monitoring and Diagnosing Faults in Induction Motors’ Three-Phase Systems Using NARX Neural Network

Valbério Gonzaga de Araújo, Aziz Oloroun-Shola Bissiriou, Juan Moises Mauricio Villanueva, Elmer Rolando Llanos Villarreal, Andrés Ortiz Salazar (), Rodrigo de Andrade Teixeira and Diego Antonio de Moura Fonsêca
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Valbério Gonzaga de Araújo: Federal Institute of Education, Science and Technology of Rio Grande do Norte (IFRN), Canguaretama 59190-000, RN, Brazil
Aziz Oloroun-Shola Bissiriou: Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (DCA-UFRN), Natal 59072-970, RN, Brazil
Juan Moises Mauricio Villanueva: Department of Electrical Engineering, Center for Alternative and Renewable Energies—CEAR, Federal University of Paraíba (CEAR-UFPB), João Pessoa 58051-900, PB, Brazil
Elmer Rolando Llanos Villarreal: Department of Natural Sciences, Mathematics, and Statistics, Federal Rural University of Semi-Arid (DCME-UFERSA), Mossoró 59625-900, RN, Brazil
Andrés Ortiz Salazar: Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (DCA-UFRN), Natal 59072-970, RN, Brazil
Rodrigo de Andrade Teixeira: Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (DCA-UFRN), Natal 59072-970, RN, Brazil
Diego Antonio de Moura Fonsêca: Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (DCA-UFRN), Natal 59072-970, RN, Brazil

Energies, 2024, vol. 17, issue 18, 1-40

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 the 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 by 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 exhibited excellent generalization across all evaluated situations, achieving the following accuracy rates: motor without fault = 94.2 %, unbalanced fault = 95%, bearings with fault = 98%, and stator with fault = 95%.

Keywords: artificial intelligence; failure classification; induction motor; artificial neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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