Integration of IoT and Digital Twin for Predictive Maintenance in Smart Manufacturing

Abstract

The convergence of Internet of Things (IoT) and Digital Twin technology in smart manufacturing systems is transforming predictive maintenance strategies. Through real-time data acquisition, simulation, and analytics, industries are now equipped to prevent equipment failures before they occur. This paper investigates the seamless integration of IoT devices with digital twins to establish a predictive maintenance framework. The study presents architectural models, data flow systems, and use case implementations that showcase real-time monitoring and simulation capabilities. It further evaluates the economic and operational benefits of deploying such technologies in a cyber-physical production ecosystem. The results demonstrate enhanced machine uptime, reduction in maintenance costs, and improved decision making, signifying a leap forward in the Industry 4.0 landscape.

Keywords: Smart Manufacturing, Predictive Maintenance, IoT Sensors, Digital Twin, Industry 4.0, Cyber-Physical Systems, Cloud Computing, Real Time Monitoring