<![CDATA[This study addresses the urgent need for timely and accurate information regarding manufacturing resources by developing a cost-effective, real-time Internet of Things (IoT)-based monitoring system for 3-phase industrial machines, such as milling, turning, and grinding equipment. The primary objective is to enhance operational efficiency, minimize downtime, and support manufacturing digitalization. The methodology employed a mixed-methods approach and utilizes a hybrid microcontroller architecture, featuring low-cost SCT-013 current sensors and ZMPT-101B voltage sensors for data capture. An Arduino Mega performs high-speed data acquisition and complex measurement calculations, while an ESP32 module handles dedicated wireless communication and transmission. The system monitors crucial electrical parameters, including phase voltage, current, active and reactive power, and energy cost, while explicitly classifying the machine state (ON/OFF/STANDBY). A MySQL database ensures reliable data storage, and a Nextion display and web interface provide real-time visualization and user control. Rigorous quantitative testing validated the implementation: the fidelity of data transfer to the database was confirmed to be 100%. Sensor readings were successfully validated against a reference AC Clamp Meter. The system efficiently supports early anomaly detection using residual analysis. However, the end-to-end system latency was measured between 5 and 6 seconds consistently. This prototype delivers an effective and reliable solution for industrial online monitoring, providing a robust, data-driven foundation for future predictive maintenance and energy efficiency strategies]]>
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