Um novo extrator de características para aplicações embarcadas de detecção de falhas em motores elétricos
Resumo
Os motores de indução são utilizados em diversas aplicações industriais devido à sua alta eficiência. Além disso, o custo por interrupção de operação não plenejada dessas máquinas costuma ser elevado. Por essa razão, há a um interesse da indústria e da comunidade científica em desenvolver soluções para detectar antecipadamente falhas nesses motores. Muitos métodos de detecção de padrões tem sido propostos a partir de diferentes sinais extraídos do motor e usando diferentes combinações de extratores de características com algoritmos de classificação alguns com elevado sucesso, como a combinação GLCM (Gray-Level Co-occurrence Matrix) e SVM (Su-pport vector machine). Este trabalho propõe uma abordagem nova para utilização do GLCM em sinais no tempo, mas evitando a criação de uma imagem intermediária para representação do sinal de entrada. A técnica proposta é avaliada em estação de trabalho e em plataforma embarcada, com resultados de taxa de acerto, tempo de computação e uso de memória.
Palavras-chave:
GLCM, Motores de indução, aprendizado de máquina, Sistemas embarcados, extrator de caracteristica
Referências
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S. Nandi and H. A. Toliyat, “Condition monitoring and fault diagnosis ofelectricalmachines-areview,”inConferenceRecordofthe1999IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No. 99CH36370), vol. 1. IEEE, 1999, pp. 197–204.
K. Pandey, P. Zope, and S. Suralkar, “Review on fault diagnosis in three-phase induction motor,” MEDHA–2012, Proceedings published by International Journal of Computer Applications (IJCA), 2012.
S. Morsalin, K. Mahmud, H. Mohiuddin, M. R. Halim, and P. Saha, “Induction motor inter-turn fault detection using heuristic noninvasive approach by artificial neural network with levenberg marquardt algorithm,” in 2014 International Conference on Informatics, Electronics & Vision (ICIEV). IEEE, 2014, pp. 1–6.
M. B. K. Bouzid and G. Champenois, “Neural network based method fortheautomatic detection ofthe stator faultsof the inductionmotor,”in 2013 International Conference on Electrical Engineering and Software Applications. IEEE, 2013, pp. 1–7.
S. S. Abdeldayem and T. Bourlai, “Automatically detecting arrhythmiarelated irregular patterns using the temporal and spectro-temporal textures of ecg signals,” in 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018, pp. 2301–2307.
R. M. Haralick, K. Shanmugam, and I. H. Dinstein, “Textural features for image classification,” IEEE Transactions on systems, man, and cybernetics, no. 6, pp. 610–621, 1973.
L.-K. Soh and C. Tsatsoulis, “Texture analysis of sar sea ice imagery using gray level co-occurrence matrices,” IEEE Transactions on geoscience and remote sensing, vol. 37, no. 2, pp. 780–795, 1999.
H. Wang, X.-H. Guo, Z.-W. Jia, H.-K. Li, Z.-G. Liang, K.-C. Li, and Q. He, “Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of ct image,” European journal of radiology, vol. 74, no. 1, pp. 124–129, 2010.
P. H. F. de Sousa, N. M. M. e Nascimento, P. P. Rebouc¸as Filho, and C. M. S. de Medeiros, “Detection and classification of faults in induction generator applied into wind turbines through a machine learning approach,” in 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018, pp. 1–7.
P. H. F. de Sousa, M. Navar de Medeiros, J. S. Almeida, P. P. Rebouc¸as Filho, V. H. C. de Albuquerque et al., “Intelligent incipient fault detection in wind turbines based on industrial iot environment,” Journal of Artificial Intelligence and Systems, vol. 1, no. 1, pp. 1–19, 2019.
R. Islam, J. Uddin, and J.-M. Kim, “Texture analysis based feature extraction using gabor filter and svd for reliable fault diagnosis of an induction motor,” International Journal of Information Technology and Management, vol. 17, no. 1-2, pp. 20–32, 2018.
W.-C. Jang, M. Kang, and J.-M. Kim, “Fault classification of an induction motor using texture features of vibration signals,” in Frontier and Innovation in Future Computing and Communications. Springer, 2014, pp. 177–183.
C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning, vol. 20, no. 3, pp. 273–297, 1995.
E. T. Silva Jr, F. Sampaio, L. C. da Silva, D. S. Medeiros, and G. P. Correia, “A method for embedding a computer vision application into a wearable device,” Microprocessors and Microsystems, p. 103086, 2020.
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S. Nandi and H. A. Toliyat, “Condition monitoring and fault diagnosis ofelectricalmachines-areview,”inConferenceRecordofthe1999IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No. 99CH36370), vol. 1. IEEE, 1999, pp. 197–204.
K. Pandey, P. Zope, and S. Suralkar, “Review on fault diagnosis in three-phase induction motor,” MEDHA–2012, Proceedings published by International Journal of Computer Applications (IJCA), 2012.
S. Morsalin, K. Mahmud, H. Mohiuddin, M. R. Halim, and P. Saha, “Induction motor inter-turn fault detection using heuristic noninvasive approach by artificial neural network with levenberg marquardt algorithm,” in 2014 International Conference on Informatics, Electronics & Vision (ICIEV). IEEE, 2014, pp. 1–6.
M. B. K. Bouzid and G. Champenois, “Neural network based method fortheautomatic detection ofthe stator faultsof the inductionmotor,”in 2013 International Conference on Electrical Engineering and Software Applications. IEEE, 2013, pp. 1–7.
S. S. Abdeldayem and T. Bourlai, “Automatically detecting arrhythmiarelated irregular patterns using the temporal and spectro-temporal textures of ecg signals,” in 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018, pp. 2301–2307.
R. M. Haralick, K. Shanmugam, and I. H. Dinstein, “Textural features for image classification,” IEEE Transactions on systems, man, and cybernetics, no. 6, pp. 610–621, 1973.
L.-K. Soh and C. Tsatsoulis, “Texture analysis of sar sea ice imagery using gray level co-occurrence matrices,” IEEE Transactions on geoscience and remote sensing, vol. 37, no. 2, pp. 780–795, 1999.
H. Wang, X.-H. Guo, Z.-W. Jia, H.-K. Li, Z.-G. Liang, K.-C. Li, and Q. He, “Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of ct image,” European journal of radiology, vol. 74, no. 1, pp. 124–129, 2010.
P. H. F. de Sousa, N. M. M. e Nascimento, P. P. Rebouc¸as Filho, and C. M. S. de Medeiros, “Detection and classification of faults in induction generator applied into wind turbines through a machine learning approach,” in 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018, pp. 1–7.
P. H. F. de Sousa, M. Navar de Medeiros, J. S. Almeida, P. P. Rebouc¸as Filho, V. H. C. de Albuquerque et al., “Intelligent incipient fault detection in wind turbines based on industrial iot environment,” Journal of Artificial Intelligence and Systems, vol. 1, no. 1, pp. 1–19, 2019.
R. Islam, J. Uddin, and J.-M. Kim, “Texture analysis based feature extraction using gabor filter and svd for reliable fault diagnosis of an induction motor,” International Journal of Information Technology and Management, vol. 17, no. 1-2, pp. 20–32, 2018.
W.-C. Jang, M. Kang, and J.-M. Kim, “Fault classification of an induction motor using texture features of vibration signals,” in Frontier and Innovation in Future Computing and Communications. Springer, 2014, pp. 177–183.
C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning, vol. 20, no. 3, pp. 273–297, 1995.
E. T. Silva Jr, F. Sampaio, L. C. da Silva, D. S. Medeiros, and G. P. Correia, “A method for embedding a computer vision application into a wearable device,” Microprocessors and Microsystems, p. 103086, 2020.
S. Datasheet, “Stmicroelectronics, 2018.”
Publicado
23/11/2020
Como Citar
DA SILVA, Lucas; SILVA JR, Elias; COSTA, Isabel.
Um novo extrator de características para aplicações embarcadas de detecção de falhas em motores elétricos. In: TRABALHOS EM ANDAMENTO - SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 10. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 192-197.
ISSN 2763-9002.
DOI: https://doi.org/10.5753/sbesc_estendido.2020.13112.
