Remote motor condition monitoring using IoT and Machine Learning: A predictive approach

A data-driven approach for predictive motor maintenance supports industrial automation. The approach leverages the integration of Internet of Things and machine learning to create a scalable solution. The system employs an array of sensors, including current, voltage, temperature, and humidity, connected to an ESP8266 microcontroller. These sensors collect operational data, which is streamed to a cloud-based platform via MQTT for continuous monitoring. The MQTT server manages data transmission through a Publish/Subscribe model, with specific topics for each sensor. A Random Forest classifier is trained on historical sensor data to predict the motor’s operational status. The model is deployed on a cloud server, allowing it to process incoming data and predict motor conditions in real-time. The application dashboard provides a user-friendly interface, displaying real-time alerts and notifications regarding motor conditions. Experimental results demonstrate the effectiveness of the machine learning model in predicting motor status with high accuracy. The proposed system contributes to the development of smart maintenance systems, promoting sustainable and efficient operation in industrial environments