Intelligent Predictive Maintenance for Motor Health Monitoring Using IoT Sensors and Machine Learning Algorithms

An Internet of Things (IoT)-based motor damage diagnosis and predictive maintenance system using machine learning (ML) for on-time motor health diagnostics. Critical information consisting of to a temperature, vibration, and RPM sensor data is retrieved from the sensors and assessed using a pre-learned machine learning model. The model analyzes and classifies the sensor data as either a “Good Product” or “Failure Product.” The system performs the vital task of failure identification, facilitating prompt system amelioration. Each sensor is connected to a central ESP-32 microcontroller which serves as the system's brain by gathering and sending the sensor data to a web server in real-time for further computation. Employed with the system is Random Forest, a classifying machine learning model that scans data to extract new patterns such as excessive vibration, overheating, and irregular changes in RPM. These sharp changes are also known as clear signs of deteriorating health of the machine. In addition to the web interface, email notification helps telemonitor system condition for most recent changes in motor health. Remediating an issue on time helps avoid device standstill and expensive damages. The life of the instrumented device is prolonged with the AI powered system controlling timely maintenance, optimizing operations. With this solution, industrial environments are more efficient as they enhance decision making and maintenance tactics since the solution is proactive, flexible, and scalable.