Implementação de um sistema de análise de vibrações mecânicas: estudo de caso

Abstract

This work presents the implementation of an intelligent mechanical vibration analysis system, utilizing IoT (Internet of Things) sensors and artificial intelligence to enhance predictive maintenance in critical industrial equipment, with a focus on cranes. Such knowledge is essential to anticipate failures before they occur, thereby reducing operational costs and increasing safety. Accelerometer sensors were installed and connected to a digital platform, enabling continuous monitoring and automatic alert generation based on vibration patterns. The results demonstrated the system's effectiveness in detecting real-time anomalies that are more difficult to identify using traditional methods, validating its technical efficiency. In the case study, the preventive replacement of a bearing avoided estimated losses of approximately R$ 12.9 million, reinforcing the positive economic impact of the solution. It is concluded that the integration of precise data and human technical expertise is essential to transform maintenance into a strategic process aligned with Industry 4.0 demands, contributing to the field of mechanical engineering by validating the application of IoT and AI technologies in industry, and providing information for replicability of the application.