Aplicação de técnicas de mineração em dados sintéticos para manutenção preditiva: um estudo de caso
Resumo
Este artigo apresenta um caso de aplicação de técnicas de mineração de dados em dados sintéticos para manutenção preditiva de um sistema de propulsão naval, com o objetivo de analisar sua aplicabilidade e adequação na construção de modelos preditivos para manutenção. Na primeira etapa, aplicamos técnicas de mineração de dados no dataset original, e levantamos hipóteses sobre os resultados obtidos com dados sintéticos. Na segunda e terceira etapas, respectivamente, testamos as hipóteses levantadas na etapa inicial através da inserção de desbalanceamento de classes e incertezas de medição. Os resultados mostram formas de se enriquecer conjuntos de dados sintéticos para construção de modelos mais parecidos com cenários industriais reais.
Palavras-chave:
manutenção preditiva, dados sintéticos, mineração de dados
Referências
Brun, K. and Kurz, R. (1998). Measurement uncertainties encountered during gas turbine driven compressor field testing. In Turbo Expo: Power for Land, Sea, and Air, volume 78644, page V003T07A001. American Society of Mechanical Engineers.
Carchiolo, V., Longheu, A., Di Martino, V., and Consoli, N. (2019). Power plants failure reports analysis for predictive maintenance. In WEBIST, pages 404-410.
Coraddu, A., Oneto, L., Ghio, A., Savio, S., Anguita, D., and Figari, M. (2016). Machine learning approaches for improving condition-based maintenance of naval propulsion plants. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 230(1):136-153.
Dalheim, and Steen, S. (2021). Uncertainty in the real-time estimation of ship speed through water. Ocean Engineering, 235:109423.
Mahmoodzadeh, Z., Wu, K.-Y., Lopez Droguett, E., and Mosleh, A. (2020). Condition-based maintenance with reinforcement learning for dry gas pipeline subject to internal corrosion. Sensors, 20(19):5708.
Mathew, V., Toby, T., Singh, V., Rao, B. M., and Kumar, M. G. (2017). Prediction of remaining useful lifetime (rul) of turbofan engine using machine learning. In 2017 IEEE International Conference on Circuits and Systems (ICCS), pages 306-311.
Mauthe, F., Hagmeyer, S., and Zeiler, P. (2021). Creation of publicly available data sets for prognostics and diagnostics addressing data scenarios relevant to industrial applications. International Journal of Prognostics and Health Management, 12(2).
Mobley, R. K. (2002). An introduction to predictive maintenance. Elsevier. Rao, S. V. (2020). Using a digital twin in predictive maintenance. Journal of Petroleum Technology, 72(08):42-44.
Schröer, C., Kruse, F., and Gómez, J. M. (2021). A systematic literature review on applying crisp-dm process model. Procedia Computer Science, 181:526-534.
Susto, G. A., Schirru, A., Pampuri, S., McLoone, S., and Beghi, A. (2015). Machine learning for predictive maintenance: A multiple classifier approach. IEEE Transactions on Industrial Informatics, 11(3):812-820.
Wirth, R. and Hipp, J. (2000). Crisp-dm: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, volume 1, pages 29-40. Manchester.
Zhang, W., Yang, D., and Wang, H. (2019). Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Systems Journal, 13(3):2213-2227.
Carchiolo, V., Longheu, A., Di Martino, V., and Consoli, N. (2019). Power plants failure reports analysis for predictive maintenance. In WEBIST, pages 404-410.
Coraddu, A., Oneto, L., Ghio, A., Savio, S., Anguita, D., and Figari, M. (2016). Machine learning approaches for improving condition-based maintenance of naval propulsion plants. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 230(1):136-153.
Dalheim, and Steen, S. (2021). Uncertainty in the real-time estimation of ship speed through water. Ocean Engineering, 235:109423.
Mahmoodzadeh, Z., Wu, K.-Y., Lopez Droguett, E., and Mosleh, A. (2020). Condition-based maintenance with reinforcement learning for dry gas pipeline subject to internal corrosion. Sensors, 20(19):5708.
Mathew, V., Toby, T., Singh, V., Rao, B. M., and Kumar, M. G. (2017). Prediction of remaining useful lifetime (rul) of turbofan engine using machine learning. In 2017 IEEE International Conference on Circuits and Systems (ICCS), pages 306-311.
Mauthe, F., Hagmeyer, S., and Zeiler, P. (2021). Creation of publicly available data sets for prognostics and diagnostics addressing data scenarios relevant to industrial applications. International Journal of Prognostics and Health Management, 12(2).
Mobley, R. K. (2002). An introduction to predictive maintenance. Elsevier. Rao, S. V. (2020). Using a digital twin in predictive maintenance. Journal of Petroleum Technology, 72(08):42-44.
Schröer, C., Kruse, F., and Gómez, J. M. (2021). A systematic literature review on applying crisp-dm process model. Procedia Computer Science, 181:526-534.
Susto, G. A., Schirru, A., Pampuri, S., McLoone, S., and Beghi, A. (2015). Machine learning for predictive maintenance: A multiple classifier approach. IEEE Transactions on Industrial Informatics, 11(3):812-820.
Wirth, R. and Hipp, J. (2000). Crisp-dm: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, volume 1, pages 29-40. Manchester.
Zhang, W., Yang, D., and Wang, H. (2019). Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Systems Journal, 13(3):2213-2227.
Publicado
19/09/2022
Como Citar
SCHENA, Rafael; NETTO, João Cesar; BECKER, Karin.
Aplicação de técnicas de mineração em dados sintéticos para manutenção preditiva: um estudo de caso. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 37. , 2022, Búzios.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 26-38.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2022.224627.
