Proposta de um algoritmo de detecção de falhas com lógica fuzzy para otimização de motores elétricos em processos de fundição de alumínio

Abstract

This study proposes the development of a fault detection system based on fuzzy logic to optimize electric motors in aluminum casting processes. The theme is grounded in the need to enhance efficiency and operational continuity in the casting industry, minimizing unplanned downtime and maintenance costs. The primary objective is to create a fuzzy control model that enables real-time monitoring of critical variables, such as temperature, pressure, and electric current, to facilitate predictive fault detection. Methodologically, operational scenario simulations were conducted to evaluate the model’s performance under adverse conditions, including overload and overheating, with validation through experimental testing. Results indicate that the proposed fuzzy system accurately identifies anomalies and issues preventive alerts, extending motor lifespan and reducing downtime. It is concluded that the model can be integrated with supervisory systems like SCADA, enhancing predictive maintenance and operational efficiency. This technological solution shows promise for the aluminum casting industry and potential applications in other industrial sectors.