PREDICTIVE MOTOR HEALTH MONITORING SYSTEM USING VIBRATION AND TEMPERATURE ANALYSIS VIA IOT

Abstract

Predictive maintenance in industrial motor systems is a critical aspect of modern manufacturing and automation, aiming to minimize unexpected downtime, reduce repair costs, and extend the operational lifespan of rotating machinery. This paper presents the design, development, and validation of a low-cost, real-time Predictive Motor Health Monitoring System (PMHMS) that leverages IoT technologies, vibration analysis, and temperature sensing to detect early-stage motor faults. The proposed system is built around the Node MCU ESP8266 microcontroller integrated with the MPU6050 six-axis inertial measurement unit for vibration and acceleration sensing, and the DHT11 digital temperature-humidity sensor for thermal monitoring. Raw vibration data is processed using Root Mean Square (RMS) analysis and frequency-domain analysis techniques to classify the operational health of the motor into three distinct states: Healthy, Warning, and Critical. The system continuously transmits sensor data over a WiFi network to a locally hosted web dashboard, providing real-time visualization of vibration trends, temperature profiles, and fault status indicators without requiring expensive cloud subscriptions or proprietary hardware. Experimental validation was conducted across ten different operating scenarios including normal operation, mechanical imbalance, bearing wear, misalignment, overheating, and compound faults. The proposed system achieved an overall classification accuracy of 96.4% with an F1-score of 96.4%, outperforming several existing low-cost monitoring solutions in terms of cost-effectiveness, ease of deployment, and real-time responsiveness. The total hardware cost of the system is approximately USD 25, making it highly accessible for small and medium-scale industrial applications. This work demonstrates that intelligent, affordable predictive maintenance is achievable using commodity IoT components, contributing meaningfully to the principles of Industry 4.0 and smart manufacturing ecosystems.