Real-Time Monitoring of Electrical Faults in Industrial Machinery Using IoT and Random Forest Regression

Abstract

The efficient operation of machinery is essential in the industrial sector, and any electrical problem may result in substantial downtime and financial losses. To identify and avoid failures in industrial equipment early on, it is essential to monitor their electrical systems in real time. In this research, provide a new method for electrical fault monitoring in real-time by combining Internet of Things (IoT) with Random Forest Regression (RFR). Industrial equipment equipped with sensors for voltage, current, temperature, and vibration may have their real-time data collected by the IoT framework. These sensors record readings from the electrical parameters in real time and send them to an analytical server. To assess the data obtained and identify electrical issues in real-time, the robust and accurate machine learning method RFR is used. To train the model and make failure predictions using real-time sensor data, the method makes use of past data. Among the many benefits of the proposed system are the following: less downtime and maintenance expenses; predictive maintenance scheduling; and early defect identification. There is less chance of equipment damage and production delays since the system can react instantly to any issues that are identified. The experimental findings show that the proposed method may successfully identify electrical problems in industrial equipment. Due to its efficiency and dependability, the system is a great instrument for improving industrial processes' performance and reliability and cutting costs and hazards associated with operations.