IoT & ML-Based Parameter Monitoring of 3-φ Induction Motors for Industrial Application

Abstract

The effective monitoring and control of numerous industrial processes have been made possible by the integration of the Internet of Things (IoT) with machine learning. The IoT and machine learning approaches are used in this research study to provide a unique method for monitoring the parameters of a three-phase induction motor. The suggested system makes use of a variety of sensors to keep track of crucial variables including temperature, humidity, vibration, voltage, and current. The collected data is subsequently transmitted to a cloud-based platform for machine learning algorithm analysis. The study's findings are utilized to forecast the motor's faults and unusual behavior, allowing for the application of corrective and predictive maintenance before any severe damage occurs. The proposed system is appropriate for numerous industrial applications since it is made to be economical and simple to install. The experimental findings indicate that the proposed framework can reliably forecast motor faults, making it a useful tool for industrial motor monitoring and control.