Manutenção preditiva aplicada ao painel elétrico de máquina de corte usada na fabricação de chicotes elétricos

Abstract

This work consists of an applied research conducted in a specific company, to solve real problems related to the maintenance of an electrical panel. The research adopted a qualitative and quantitative approach, using bibliographic, documentary, and field sources to obtain the necessary data. Company X, which served as the basis for the study, is a leading independent manufacturer in the automotive components sector, known for its excellence in producing automotive whips and a variety of other high-quality products for the automotive industry. The main objective of the project is to collect real-time data on the temperature of the electrical panel and store it in a cloud database. This allows for the analysis of the machine's behavior and the detection of disturbances, enabling preventive actions before a failure occurs. When the temperature exceeds the limit established by the manufacturer, an alert email is automatically sent, allowing for immediate checks and interventions. The Firebase real-time database is used for data storage, and the IFTTT platform is employed to establish the connection and send the alert email. With this approach, the project aims to ensure continuous collection of accurate temperature data from the electrical panel, enabling in-depth analysis and proactive detection of potential failures. The results obtained from implementing this project demonstrated the importance of predictive practices and the use of advanced technologies in asset management and the pursuit of operational efficiency. There was a significant reduction in maintenance costs, as well as a positive impact on the availability and reliability of the machine. Early identification of faults and the implementation of preventive actions prevented unplanned stops and interruptions in production, increasing the company's production capacity and improving its efficiency and reliability. In addition to the financial and operational benefits, the implementation of Predictive Maintenance also contributed to the company's image. By adopting modern practices and advanced technologies, the organization demonstrated its commitment to efficiency, quality, and sustainability. This strengthened its position in the market, making it more competitive and gaining the trust and loyalty of customers