AI-Enabled Predictive Maintenance for Industrial Equipment in the Era of IoT

Modern digital technology breakthroughs include machine learning. It's used in medical, image processing, manufacturing, aviation, autonomics, and more. Our main goal is to find an automated system that can accurately rate production machines at any given moment. The critical energy meter characteristics needed to create power reports in automation system to solve production difficulties at defined intervals were also not captured. We apply machine learning to estimate manufacturing machine accuracy in this study. We employed supervised machine learning of binary decision tree using CART method and power report, obtained data using RS232 to RS485 convertor through Modbus communication protocol, to overcome these challenges. This study presents issue formulation, energy meter data analysis and retrieval, and ML approaches used to estimate production machine accuracy. After retrieving the parameters, we provide power reports for each machine and a graphical warning of deteriorating performance.