Intelligent Industrial Motor Fault Diagnosis and Predictive Maintenance using Multi-Sensor Fusion and Machine Learning

Abstract

Industrially used electric motors are crucial parts of industries and process industries, which failure might lead to significant financial losses due to the unplanned downtime. Current methods of motor maintenance based on regular inspections and reactive maintenance strategies are unable to detect the early symptoms of the motor condition deterioration. This paper describes the development of the Intelligent Industrial Motor Fault Diagnosis and Predictive Maintenance System using the principles of data fusion from multi-sensors and machine learning for monitoring of the motor condition and its further faults prediction. The proposed system includes ESP32 microcontroller along with multiple sensors measuring the important operating parameters, namely motor current, vibration, rotation speed (RPM), and gyroscope movement. The Wokwi simulation environment has been created to mimic various operating states of the motor, namely, normal, overloaded, vibrations and fault conditions. The collected sensor data are used as the training dataset for a machine learning model to automatically predict the state of the motor health. The proposed framework provides the ability to use simulation data generation along with machine learning algorithms for a cost-efficient intelligent monitoring of motor conditions without the need for expensive industrial testing setups. The proposed methodology is a solid base for future work and can be implemented in industry using IoT remote monitoring and cloud predictive maintenance.