Análise preditiva a partir da caracterização das emissões gasosas e do óleo lubrificante em frotas com motorização a diesel - Sousa, Eduardo Henrique Viana de (2010)
This work was motivated by the importance of conducting a study of vehicle emissions in captive fleets with diesel engine, coupled with the predictive maintenance plan. This type of maintenance includes techniques designed to meet the growing market demand to reduce maintenance costs by increasing the reliability of diagnoses, which has increased interest in automated predictive maintenance on diesel engines, preventing problems that might evolve into routine turn into serious situations, solved only with complex and costly repairs, the Reliability Centered Maintenance, will be the methodology that will make our goal is reached, beyond maintaining the vehicles regulated as fuel consumption and emissions. To Therefore, technical improvements were estimated capable of penetrating the automotive market and give the inshore fleet emission rates of opacity of the vehicles, being directly related to the conditions of the lubricating oil thus contributing to reducing maintenance costs by contributing significantly to emissions of pollutants and an improvement in the air in large cities. This criterion was adopted and implemented, em 241 buses and produced a diagnosis of possible failures by the correlation between the characterization of used lubricating oils and the analysis of opacity, with the objective of the aid the detection and solution of failures for the maintenance of sub-systems according to design criteria, and for this to be a deductive methodology to determine potential causes of failures, has been automated to implement a predictive maintenance system for this purpose was used in our study a mobile unit equipped with a opacimeter and a kit for collection and analysis of lubricating oil and the construction of the network diagnostics, we used a computer program in Microsoft Office Access 2007 platform tool is indispensable for creating a database data, this method is being used and successfully implemented in seven (7) bus companies from the city of Natal (RN) Brazil