FAILURE ANALYSIS OF ELECTRIC MOTORS USING PREDICTIVE MAINTENANCE TECHNIQUES

Abstract

The growing trend toward industrial automation has heightened the importance of production equipment reliability, especially on manufacturing lines that operate continuously. In this context, electric motors play a fundamental role in driving material handling and transportation systems. Failures in this equipment can lead to production losses, increased maintenance costs, and compromised process quality. Among the most commonly used techniques in predictive maintenance are vibration analysis, infrared thermography, electrical current monitoring, and condition analysis of mechanical components, according to Mobley (2021). The study consisted of analyzing the occurrence of progressive failures in an electric motor responsible for driving a conveyor belt at a motorcycle parts manufacturer located in the Manaus Industrial Complex. The methodology adopted consisted of applied quantitative research, using simulated operational data obtained by monitoring the variables of vibration, temperature, and electrical current over six consecutive weeks of operation. Reliability and availability indicators were calculated, and the evolution of the monitored variables was analyzed. The results showed that vibration exhibited the highest percentage increase, reaching 142.8%, followed by temperature at 36.2% and electrical current at 25%. The study concludes that the implementation of predictive maintenance techniques based on vibration, temperature, and electrical current sensors can significantly reduce unscheduled downtime, increase operational reliability, and improve the productivity of the production system.