PROPOSAL FOR AUTOMATED SOLUTION FOR PREDICTIVE MONITORING OF CONVEYOR BELT

Abstract

This study proposes an automated solution for predictive monitoring of conveyor belts. The main objective was to demonstrate the application of modern sensors to measure thickness and early detect wear, aiming to extend the lifespan of the equipment and ensure operational continuity. The methodology involved the installation of ultrasonic and laser sensors at critical points of the belts, integrating the data into a supervisory platform for real-time analysis. Data mining techniques and machine learning algorithms were applied to identify wear patterns and predict remaining useful life. The main results obtained include a reduction in unplanned downtime, improved maintenance scheduling management, and prolonged lifespan of the belts. Tests in a simulated environment confirmed the robustness of the sensors under extreme conditions. In conclusion, the solution proved effective in enhancing predictive maintenance.