Digital Twin-Based Real-Time Industrial Monitoring and Control

The study presents an industrial motor real-time monitoring and control system based on digital twin implementations. The system maintains ongoing tracking of crucial parameters, including temperature, vibration, and sound, by using IoT sensors; therefore, it detects problems beforehand and supports maintenance strategies before problems arise. Operating temperatures between 20°C and 40°C fall within the normal range, while 40°C to 50°C stands as the warning level, and critical conditions exist between the temperatures of 50°C and 60°C, triggering automated shutdown functions to protect machinery. The platform uses microcontroller processing of sensor data to deliver these data through a cloud platform, which maintains three-dimensional digital twin models for remote visual inspection and real-time tracking purposes. Experimental tests validate that this system detects potential issues swiftly along with reducing manual visibility checks, which results in better operational safety outcomes and enhanced operational efficiency. The implementation of Digital Twin technology and IoT-based monitoring produces results that support predictive maintenance together with enhanced asset management and intelligent industrial environment development.