The expansion of services and technological equipment applied to the agribusiness sector grows year after year, e.g., tractors and agricultural machinery, which use systems shipped with sophisticated software that collaborate to aid, and optimize activities in the field. Maintenance of agricultural machinery, including tractors, is routine in the life of any farmer, especially the preventive and corrective maintenance. In this paper, the objective was to evaluate an alternative for the use of diagnostic software in the prediction of failures that may occur in the tractor clutch system. In this study, the PicoScope6 software was used to identify the failures in this system, and then, using the predictive maintenance phase, was compared with classic maintenance methods, allowed for estimating the necessary repair time. Results showed that software identified more precisely the mechanical components that presented failures. From the identification of failure components, a list of repairs and exchanges was estimated, and, when compared to the list of components generated by inspection without diagnostic software, the repair time was reduced by 88%, and the cost of repair in up to 93%. The availability of the equipment also increased as a result of the shorter repair time, thus maximizing the machine time in the field.
Health monitoring of automotive clutch system by using Bayes algorithms
