IoT based Predictive Maintenance System for Industrial Motors Using Raspberry Pi and Edge Analytics

Abstract: Predictive maintenance is a key factor in ensuring the reliability and efficiency of industrial motors. It helps in detecting potential faults in machines before they occur. In this paper, a predictive maintenance system using IoT technology is proposed to ensure the health monitoring of industrial motors using a Raspberry Pi and sensor technology. In this system, motor parameters such as vibration, temperature, and current are considered to analyze the motor’s operating condition. Experimental tests were carried out under various operating conditions such as normal operation, increased load, and fault condition using a motor. The proposed system detected abnormal operating conditions such as bearing fault, temperature rise, and current overload when vibration is above 1.0 g, temperature exceeds 65°C, and current exceeds 4 A, respectively. The system was able to achieve a fault detection accuracy of approximately 94.5%, with a response time of less than two seconds. Thus, it is clear that the proposed system is a reliable, cost-effective, and efficient solution for the purpose of motor condition monitoring.

Keywords: Internet of Things, Raspberry Pi, Predictive Maintenance, Industrial Motors