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English Information

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Authors
# Name
1 Rafael Schena(rafael.schena@inf.ufrgs.br)
2 João Cesar Netto(netto@inf.ufrgs.br)
3 Karin Becker(karin.becker@inf.ufrgs.br)

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Reference
# Reference
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2 Carchiolo, V., Longheu, A., Di Martino, V., and Consoli, N. (2019). Power plants failure reports analysis for predictive maintenance. In WEBIST, pages 404–410.
3 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.
4 Dalheim, Ø. Ø. and Steen, S. (2021). Uncertainty in the real-time estimation of ship speed through water. Ocean Engineering, 235:109423.
5 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.
6 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.
7 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).
8 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.
9 Schroer, C., Kruse, F., and G ́omez, J. M. (2021). A systematic literature review on ap- plying crisp-dm process model. Procedia Computer Science, 181:526–534.
10 Susto, G. A., Schirru, A., Pampuri, S., McLoone, S., and Beghi, A. (2015). Machine le- arning for predictive maintenance: A multiple classifier approach. IEEE Transactions on Industrial Informatics, 11(3):812–820.
11 Wirth, R. and Hipp, J. (2000). Crisp-dm: Towards a standard process model for data mi- ning. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, volume 1, pages 29–40. Manchester.
12 Zhang, W., Yang, D., and Wang, H. (2019). Data-driven methods for predictive mainte- nance of industrial equipment: A survey. IEEE Systems Journal, 13(3):2213–2227.