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Condition Monitoring with Prediction Based on Diesel Engine Oil Analysis: A Case Study for Urban Buses

1
Centre for Mechanical Engineering, Materials and Processes (CEMMPRE), University of Coimbra, 3030-788 Coimbra, Portugal
2
Instituto Superior de Engenharia de Coimbra, Polytechnic of Coimbra, 3030-199 Coimbra, Portugal
3
Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
*
Author to whom correspondence should be addressed.
Actuators 2019, 8(1), 14; https://doi.org/10.3390/act8010014
Received: 14 November 2018 / Revised: 22 January 2019 / Accepted: 13 February 2019 / Published: 16 February 2019
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Abstract

This paper presents a case study and a model to predict maintenance interventions based on condition monitoring of diesel engine oil in urban buses by accompanying the evolution of its degradation. Many times, under normal functioning conditions, the properties of the lubricants, based on the intervals that manufacturers recommend for its change, are within normal and safety conditions. Then, if the lubricants’ oil condition is adequately accompanied, until reaching the degradation limits, the intervals of oil replacement can be enlarged, meaning that the buses’ availability increases, as well as their corresponding production time. Based on this assumption, a mathematical model to follow and to manage the oil condition is presented, in order to predict the next intervention with the maximum time between them, which means the maximum availability. View Full-Text
Keywords: condition monitoring; predictive maintenance; oil analysis; urban buses condition monitoring; predictive maintenance; oil analysis; urban buses
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Raposo, H.; Farinha, J.T.; Fonseca, I.; Ferreira, L.A. Condition Monitoring with Prediction Based on Diesel Engine Oil Analysis: A Case Study for Urban Buses. Actuators 2019, 8, 14.

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