Predictive Maintenance for Industrial Equipment (Motor) Using IoT

This paper generally bring in a smart system that predicts when industrial motors need maintenance using machine learning. Traditional maintenance methods, like waiting for something to disrupt or following a set schedule, can lead to unexpected downtime and higher repair costs. This research explores a predictive approach that influences real-time data from vibration, current, and temperature sensors installed on motors. The data is analyzed by smart computer programs to find unusual patterns and guess when equipment might break, so repairs can be done before any problems happen. Predictive maintenance systems commonly use machine learning algorithms to forecast equipment failures. When comparing the algorithms, the Random Forest (RF) algorithm often demonstrates the highest accuracy in predicting motor failures. A data-driven approach to predictive maintenance can significantly reduce downtime and lower maintenance costs by enabling more efficient scheduling and better-informed decisions. By combining Industrial Internet of Things (IIoT) technologies, MQTT messaging, and machine learning, industrial motor maintenance can be improved, which aligns with the goals of Industry 4.0 for sustainable. Predictive maintenance helps to reduce maintenance cost.