Abstract:
Industrial automation's dependence on conventional human techniques has long been linked to inefficiencies, higher operating costs, and a higher chance of equipment failure. This demonstrates the urgent need for creative solutions that can lower costs and improve operational efficiency in the processing and manufacturing sectors. The creation of a Smart Industrial Machine Control and Monitoring System is a crucial step in the advancement of industrial automation to handle these issues. The goal of this system is to update the operation and management of vital machinery, such industrial air compressors, which are essential to many different sectors. Lack of predictive maintenance, lack of real-time monitoring, and restricted control over operational parameters are the main problems this technology attempts to solve. These difficulties have a major impact on productivity and profitability because they increase downtime, energy inefficiency, and non-compliance with safety regulations. To address these issues, the suggested approach makes use of machine learning, IoT technologies, and microcontroller-based control systems. While machine learning algorithms evaluate data to forecast problems and improve maintenance plans, sensors keep an eye on critical parameters like temperature, vibration, oil level, and voltage supply. NodeMCU modules offer remote access and real-time data collecting, which facilitates effective administration and prompt intervention through alert notifications. This technology improves energy efficiency, operational lifespan, and cost-effectiveness in addition to guaranteeing adherence to safety rules. The study shows how digital solutions in industrial automation may revolutionize air compressor management, opening the door to greater sustainability and competitiveness in the industrial sector
