Desenvolvimento de aplicativo para gestão de banco de óleo hidráulico para análise preditiva

Abstract

With the emergence and expansion of computers, maintenance has become even more sophisticated. Technology has become part of the daily planning, control and analysis of maintenance. Predictive analytics is the process of using data to predict future outcomes. The process uses data analysis, machine learning, artificial intelligence and statistical models to find patterns that can predict future behavior. The objective of this work is to develop an application using the Power Apps and Power BI platform to manage a hydraulic oil bank and, in this way, facilitate the preventive and predictive maintenance of an agricultural maintenance workshop, aiming at reducing costs, machine downtime, waste and quality in production. hydraulic hydration in fleets. In addition to the control of movement and maintenance logistics in fleets through the application, Weibull statistical analyzes are performed in Python programming languages for management focused on reliability engineering. When applying this application, a reduction in hydraulic oil consumption is foreseen, confident with the environment and the company's budget, approaching 80% of the amount spent on hydraulic oil changes. In addition, it contributes to reducing the consumption of components hydraulic systems and, also, for the reduction of breaks in hydraulic systems caused by contamination of deficient hydraulic oil.