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.
Highlights
Public transportation, in general, and city bus passenger transportation, in particular, represents an important alternative to the use of individual transportation
Some relevant topics about related works on condition monitoring and predictive maintenance based on oil analysis, in urban buses fleets, are mentioned and discussed in [22,23,24,25,26]
The paper starts with a global analysis on the importance of oil analysis in predictive maintenance based on condition monitoring
Summary
In general, and city bus passenger transportation, in particular, represents an important alternative to the use of individual transportation. Several authors are not in agreement with the influence of this new type of fuel on oil degradation and, by consequence, on the maintenance based on condition monitoring. Some relevant topics about related works on condition monitoring and predictive maintenance based on oil analysis, in urban buses fleets, are mentioned and discussed in [22,23,24,25,26]. This paper presents an approach to the analysis of lubricating oils for Diesel engines, through mathematical and statistical models, namely: exponential smoothing; t-Student distribution; and hypotheses tests validated on some models of urban buses, as it is described throughout the paper. The paper starts with a global analysis on the importance of oil analysis in predictive maintenance based on condition monitoring. The variable Fe is summarily presented as example; Due to the variation of the variable Fe, a t-Student distribution with bilateral test of hypotheses is used; an example using several values for smoothing parameter and some levels of significance is presented; the influence of the maintenance policy, namely the predictive in the reserve fleet is discussed
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