Abstract
In this work we present the development of nonlinear autoregressive with exogenous inputs models to predict some relevant variables for asset management of heavy mining equipment, like Mean Time between Failures (MTBF), Mean Time to Repair (MTTR) and Availability is presented. The models were developed using support vector machine with historical data obtained on a daily basis during 2013 from one heavy mining equipment of an important copper mine site in Chile. One-step-ahead predictions of the predicted variables confirmed good performance of the dynamic models.
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Acuña, G., Curilem, M., Araya, B., Cubillos, F., Miranda, R., Garrido, F. (2014). Predictive Models Applied to Heavy Duty Equipment Management. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds) Nature-Inspired Computation and Machine Learning. MICAI 2014. Lecture Notes in Computer Science(), vol 8857. Springer, Cham. https://doi.org/10.1007/978-3-319-13650-9_18
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DOI: https://doi.org/10.1007/978-3-319-13650-9_18
Publisher Name: Springer, Cham
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