Fuzzy System for Fault Detection in Electric Motors for Aluminum Casting

Abstract

This study presents the development of a fuzzy logic-based system for predictive fault detection in electric motors used in aluminum casting processes. The addressed problem concerns the need to optimize predictive maintenance in a competitive industrial environment, minimizing unexpected downtimes and costs associated with corrective maintenance. The main objective was to create a fuzzy algorithm for real-time monitoring of critical variables such as temperature, pressure, and electric current. The methodology involved simulations of operational scenarios validated through experimental tests in a controlled environment. Results indicate that the proposed fuzzy system accurately identifies anomalies and issues preventive alerts, contributing to extending motor lifespan and improving operational efficiency. It is concluded that the developed solution can be integrated into industrial supervisory systems, enhancing reliability and productivity.