Fault detection for rotating machinery based on vibration data using machine learning
Resumo
Este artigo aborda a detecção de falhas mecânicas em máquinas rotativas usando técnicas de aprendizado de máquina. Foram coletados sinais de vibração das máquinas em operação na indústria e extraídas features desses sinais, desde harmônicas da velocidade de rotação do motor até features especializadas tipicamente consideradas por analistas de vibração. Após limpeza e pré-processamento dos dados, foi construída uma pipeline de treinamento e otimização de hiperparâmetros. Foram explorados modelos como regressão logística, máquinas de vetores de suporte, florestas aleatórias, redes neurais e gradient boosting (XGBoost). Os resultados mostraram que o modelo XGBoost obteve o melhor desempenho, alcançando uma métrica de ROC AUC de 91%.
Palavras-chave:
Detecção de falhas, análise de vibração, máquinas rotativas, aprendizado de máquina
Referências
Ahmed, H. and Nandi, A. K. (2020). Condition monitoring with vibration signals: Compressive sampling and learning algorithms for rotating machines. John Wiley & Sons.
Alharbi, F., Luo, S., Zhang, H., Shaukat, K., Yang, G., Wheeler, C. A., and Chen, Z. (2023). A brief review of acoustic and vibration signal-based fault detection for belt conveyor idlers using machine learning models. Sensors, 23(4):1902.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, Berlin, Heidelberg.
Boashash, B. (2015). Time-frequency signal analysis and processing: a comprehensive reference. Academic press.
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., and Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408:189–215.
Chen, T. and Guestrin, C. (2016). XGBoost. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM.
Gao, Z., Cecati, C., and Ding, S. X. (2015). A survey of fault diagnosis and fault-tolerant techniques—part i: Fault diagnosis with model-based and signal-based approaches. IEEE transactions on industrial electronics, 62(6):3757–3767.
Géron, A. (2019). Hands-on Machine Learning with Scikit-Learn, Keras, and Tensor-Flow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media, Incorporated.
Janssens, O., Slavkovikj, V., Vervisch, B., Stockman, K., Loccufier, M., Verstockt, S., Van de Walle, R., and Van Hoecke, S. (2016). Convolutional neural network based fault detection for rotating machinery. Journal of Sound and Vibration, 377:331–345.
Laguna, C. and Lerch, A. (2016). An efficient algorithm for clipping detection and declipping audio. In Audio Engineering Society Convention 141. Audio Engineering Society.
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., and Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138:106587.
Randall, R. B. (2021). Vibration-based condition monitoring: industrial, automotive and aerospace applications. John Wiley & Sons.
Welch, P. D. (1967). The use of fast fourier transforms for the estimation of power spectra: A method based on time averaging over short modified periodograms. IEEE Transactions on Audio and Electroacoustics, 15:70–73.
Zimroz, R. and Król, R. (2009). Failure analysis of belt conveyor systems for condition monitoring purposes. Mining Science, 128(36):255.
Alharbi, F., Luo, S., Zhang, H., Shaukat, K., Yang, G., Wheeler, C. A., and Chen, Z. (2023). A brief review of acoustic and vibration signal-based fault detection for belt conveyor idlers using machine learning models. Sensors, 23(4):1902.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, Berlin, Heidelberg.
Boashash, B. (2015). Time-frequency signal analysis and processing: a comprehensive reference. Academic press.
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., and Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408:189–215.
Chen, T. and Guestrin, C. (2016). XGBoost. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM.
Gao, Z., Cecati, C., and Ding, S. X. (2015). A survey of fault diagnosis and fault-tolerant techniques—part i: Fault diagnosis with model-based and signal-based approaches. IEEE transactions on industrial electronics, 62(6):3757–3767.
Géron, A. (2019). Hands-on Machine Learning with Scikit-Learn, Keras, and Tensor-Flow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media, Incorporated.
Janssens, O., Slavkovikj, V., Vervisch, B., Stockman, K., Loccufier, M., Verstockt, S., Van de Walle, R., and Van Hoecke, S. (2016). Convolutional neural network based fault detection for rotating machinery. Journal of Sound and Vibration, 377:331–345.
Laguna, C. and Lerch, A. (2016). An efficient algorithm for clipping detection and declipping audio. In Audio Engineering Society Convention 141. Audio Engineering Society.
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., and Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138:106587.
Randall, R. B. (2021). Vibration-based condition monitoring: industrial, automotive and aerospace applications. John Wiley & Sons.
Welch, P. D. (1967). The use of fast fourier transforms for the estimation of power spectra: A method based on time averaging over short modified periodograms. IEEE Transactions on Audio and Electroacoustics, 15:70–73.
Zimroz, R. and Król, R. (2009). Failure analysis of belt conveyor systems for condition monitoring purposes. Mining Science, 128(36):255.
Publicado
25/09/2023
Como Citar
BARRETO, Lucas de T.; ROSA, Rodrigo K.; SILVA, Danilo; BRAGA, Danilo.
Fault detection for rotating machinery based on vibration data using machine learning. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 242-256.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2023.233935.
