Processamento de Sinais de Vibração aplicado à Classificação de Falhas em Rolamentos
Resumo
Esse trabalho compara o desempenho de diversos algoritmos de classificação aplicados ao diagnóstico de falhas em rolamentos. Para a construção dos modelos, 13 descritores estatísticos foram extraídos dos sinais de vibração disponíveis no conjunto de dados Paderborn. Os modelos foram construídos no domínio do tempo e no domínio tempo-escala com a utilização da transformada wavelet, e foram aplicados os algoritmos k-NN, SVM e Árvore de Decisão. Os desempenhos dos modelos foram avaliados com base nas métricas de acurácia, precisão, sensibilidade, especificidade e F1-score. O resultado médio obtido em todas as configurações dos classificadores foi 98%.
Referências
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Barcelos, A. D. S., Mazzoni, F. M., and Cardoso, A. J. M. (2020). Análise de avarias em rolamentos, utilizando algoritmos de inteligência artificial. In Anais do Congresso Brasileiro de Automática 2020. sbabra.
Eren, L., Ince, T., and Kiranyaz, S. (2018). A generic intelligent bearing fault diagnosis system using compact adaptive 1d cnn classifier. Journal of Signal Processing Systems.
Gongora, W. S., Goedtel, A., Castoldi, M. F., Oliveira da silva, S. A., and Da Silva, I. N. (2018). Embedded system to detect bearing faults in line-connected induction motors. In 2018 XIII International Conference on Electrical Machines (ICEM), pages 1841–1847.
Islam, R., Khan, S. A., and Kim, J.-M. (2016). Discriminant feature distribution analysis-based hybrid feature selection for online bearing fault diagnosis in induction motors. Journal of Sensors, 2016:1–16.
Lei, Y. (2016). Intelligent fault diagnosis and remaining useful life prediction of rotating machinery. Butterworth-Heinemann.
Lessmeier, C., Kimotho, J. K., Zimmer, D., and Sextro, W. (2016). Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for Data-Driven classification”. PHM Society European Conference, 3.
Lu, S., Qian, G., He, Q., Liu, F., Liu, Y., and Wang, Q. (2020). In situ motor fault diagnosis using enhanced convolutional neural network in an embedded system. IEEE Sensors Journal, 20(15):8287–8296.
Mallat, S. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7):674–693.
Markiewicz, M., Wielgosz, M., Bocheński, M., Tabaczyński, W., Konieczny, T., and Kowalczyk, L. (2019). Predictive maintenance of induction motors using ultra-low power wireless sensors and compressed recurrent neural networks. IEEE Access, 7:178891–178902.
MATLAB (2010). version 9.8.0 (R2020a). The MathWorks Inc., Natick, Massachusetts.
Ren, Y., Shi, S., Yang, L., Lin, J., and Guo, S. (2018). Rotating machinery vibration analysis of the rotary-laser scanning measurement system. In Zhu, J., Xu, K., Dong, L., Tam, H.-Y., and Xiao, H., editors, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems. SPIE.
Ribeiro, F., Marins, M., Netto, S., and da Silva, E. (2017). Rotating machinery fault diagnosis using similarity-based models.
Strang, G. and Nguyen, T. (1996). Wavelets and filter banks. Wellesley-Cambridge Press, Wellesley, MA, 2 edition.
Toma, R. N. and Kim, J.-M. (2020). Bearing fault classification of induction motors using discrete wavelet transform and ensemble machine learning algorithms. Applied Sciences, 10(15):5251.
Toma, R. N., Prosvirin, A. E., and Kim, J.-M. (2020). Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors, 20(7):1884.
Tsypkin, M. (2013). Induction motor condition monitoring: Vibration analysis technique a twice line frequency component as a diagnostic tool. In 2013 International Electric Machines & Drives Conference. IEEE.
Zhang, S., Zhang, S., Wang, B., and Habetler, T. G. (2020). Deep learning algorithms for bearing fault diagnostics—a comprehensive review. IEEE Access, 8:29857–29881.
Barcelos, A. D. S., Mazzoni, F. M., and Cardoso, A. J. M. (2020). Análise de avarias em rolamentos, utilizando algoritmos de inteligência artificial. In Anais do Congresso Brasileiro de Automática 2020. sbabra.
Eren, L., Ince, T., and Kiranyaz, S. (2018). A generic intelligent bearing fault diagnosis system using compact adaptive 1d cnn classifier. Journal of Signal Processing Systems.
Gongora, W. S., Goedtel, A., Castoldi, M. F., Oliveira da silva, S. A., and Da Silva, I. N. (2018). Embedded system to detect bearing faults in line-connected induction motors. In 2018 XIII International Conference on Electrical Machines (ICEM), pages 1841–1847.
Islam, R., Khan, S. A., and Kim, J.-M. (2016). Discriminant feature distribution analysis-based hybrid feature selection for online bearing fault diagnosis in induction motors. Journal of Sensors, 2016:1–16.
Lei, Y. (2016). Intelligent fault diagnosis and remaining useful life prediction of rotating machinery. Butterworth-Heinemann.
Lessmeier, C., Kimotho, J. K., Zimmer, D., and Sextro, W. (2016). Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for Data-Driven classification”. PHM Society European Conference, 3.
Lu, S., Qian, G., He, Q., Liu, F., Liu, Y., and Wang, Q. (2020). In situ motor fault diagnosis using enhanced convolutional neural network in an embedded system. IEEE Sensors Journal, 20(15):8287–8296.
Mallat, S. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7):674–693.
Markiewicz, M., Wielgosz, M., Bocheński, M., Tabaczyński, W., Konieczny, T., and Kowalczyk, L. (2019). Predictive maintenance of induction motors using ultra-low power wireless sensors and compressed recurrent neural networks. IEEE Access, 7:178891–178902.
MATLAB (2010). version 9.8.0 (R2020a). The MathWorks Inc., Natick, Massachusetts.
Ren, Y., Shi, S., Yang, L., Lin, J., and Guo, S. (2018). Rotating machinery vibration analysis of the rotary-laser scanning measurement system. In Zhu, J., Xu, K., Dong, L., Tam, H.-Y., and Xiao, H., editors, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems. SPIE.
Ribeiro, F., Marins, M., Netto, S., and da Silva, E. (2017). Rotating machinery fault diagnosis using similarity-based models.
Strang, G. and Nguyen, T. (1996). Wavelets and filter banks. Wellesley-Cambridge Press, Wellesley, MA, 2 edition.
Toma, R. N. and Kim, J.-M. (2020). Bearing fault classification of induction motors using discrete wavelet transform and ensemble machine learning algorithms. Applied Sciences, 10(15):5251.
Toma, R. N., Prosvirin, A. E., and Kim, J.-M. (2020). Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors, 20(7):1884.
Tsypkin, M. (2013). Induction motor condition monitoring: Vibration analysis technique a twice line frequency component as a diagnostic tool. In 2013 International Electric Machines & Drives Conference. IEEE.
Zhang, S., Zhang, S., Wang, B., and Habetler, T. G. (2020). Deep learning algorithms for bearing fault diagnostics—a comprehensive review. IEEE Access, 8:29857–29881.
Publicado
25/10/2022
Como Citar
CAMPOS, Rodrigo C.; DO NASCIMENTO, Gizele P.; DALPRÁ, Hudson C.; PINTO, Luiz A..
Processamento de Sinais de Vibração aplicado à Classificação de Falhas em Rolamentos. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 10. , 2022, Goiás.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 60-71.
DOI: https://doi.org/10.5753/erigo.2022.227668.
