Using Digital Twin to Diagnose Faults in Braiding Machinery Based on IoT

The digital twin (DT) includes real-time data analytics based on the actual product or manufacturing processing parameters. Data from digital twins can predict asset maintenance requirements ahead of time. This saves money by decreasing operating expenses and asset downtime, which improves company efficiency. In this paper, a digital twin in braiding machinery based on IoT (DTBM-IoT) used to diagnose faults. When an imbalance fault occurs, the system gathers experimental data. After that, the information is sent into a digital win model of the rotor system to see whether it can quantify and locate imbalance for defect detection. It is possible to anticipate asset maintenance requirements with DT technology by IoT (Internet of Things) sensors, XR(XRay) capabilities, and AI-powered analytics. A DT model's appropriate design and flexibility remain difficult because of the nonlinear dynamics and unpredictability inherent in the degrading process of equipment. The results indicate that the DT in braiding machinery developed allows for precise diagnostic and dynamic deterioration analysis. At least there is 37% growth in efficiency over conventional approaches.