Abstract
A model of the running resistance of a locomotive powered by liquefied natural gas is proposed. The model uses operating data and does not require specific instrumentation. The input data consists of a succession of instantaneous speed and electrical power measurements of a diesel-electric locomotive. The slope at each point along the route is unknown and the speed is measured with a digital sensor that quantifies the signal, so acceleration estimates are also unreliable. From these data, a weakly supervised learning problem is defined that makes use of a fuzzy rule-based system to indirectly predict the effective slope, and is able to estimate the power demand of the locomotive with a margin of error close to 5%.
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Acknowledgements
This work has been partially funded by grants from the Spanish Ministry of Economy, Industry and Competitiveness (ref. PID2020-112726-RB-I00) and the Principality of Asturias (ref. SV-PA-21-AYUD/2021/50994).
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Sánchez, L., Luque, P., Álvarez-Mántaras, D., Otero, J., Costa, N. (2023). Weakly Supervised Learning of the Motion Resistance of a Locomotive Powered by Liquefied Natural Gas. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_59
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DOI: https://doi.org/10.1007/978-3-031-18050-7_59
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