Monitoreo en tiempo real de motores eléctricos para el mantenimiento predictivo y detección de fallas.

Abstract

Abstract: Today, technology plays a crucial role in optimizing industrial processes, especially in machine management. Electric motors are essential components in various sectors, such as manufacturing, power generation, and automation. However, their inevitable wear requires effective maintenance strategies to maximize their lifespan and minimize failures. This work proposes the development of a real-time monitoring system for predictive maintenance of electric motors, based on the integration of sensors, data analytics, and cloud platforms. By collecting critical parameters such as vibration, temperature, and current, the system employs predictive algorithms to detect anomalies and prevent failures. The implementation uses Google Cloud for data storage and processing, along with microcontrollers for signal acquisition. The results demonstrate that this approach improves motor reliability, reduces costs associated with unplanned downtime, and optimizes industrial productivity.