A hybrid framework of IoT and machine learning for predictive analytics of a DC motor

Abstract

Many industrial applications utilize direct current (DC) motor as an essential element. It functions as the backbone of several industries and global pillar of manufacturing applications. The predictive analytics of motor is primary for preventing unpredicted downtime, reducing protection costs, and improving system effectiveness. This paper presents a hybrid framework integrating the internet of things (IoT) and machine learning (ML) for real-time predictive analytics of DC motors. The leveraging of machine learning algorithms in predictive maintenance of DC motors has shown significant potential in reducing downtime and increasing the lifespan of the motor. Therefore, a system for predictive analytics with machine learning strategy is proposed and message queuing telemetry transport (MQTT messaging) is included for effective information transmission between sensors and gateways. The data received from the sensors is utilized to make prediction about the remaining useful life of the motor and generate alerts for maintenance before failures occur. So, the integration of machine learning algorithms in predictive maintenance of DC motors is a promising approach to increase the reliability and efficiency of DC motors. The highest performance is achieved in random forest with accuracy of 93.4%.