A Data-Driven Digital Twin Architecture for Failure Prediction of Customized Automatic Transverse Robot

Abstract

The customized automatic transverse robots play important roles in transporting heavy printing material rolls in automatic and intelligent flexible packaging printing workshops. A three-dimensional (3D) real-time visualization monitoring and feedback system of the customized automatic transverse robot is developed by adopting digital twin technology. This system is based on the theoretical framework of the data-driven digital twin model, and the main functions work through the physical layer, digital twin layer, and functional layer. The twin model is established and connected through the parent-child matching relationship of the components and it is driven by the real-time data which are acquired, transmitted and stored through communication protocols, such as OPC UA and RS-485. The physical automatic transverse robot and the virtual world of digital twins are synchronously mapped. The data are transferred from the virtual world twin model to the real world by establishing the application programming interface (API), implementing a feedback regulation mechanism that controls reality with virtuality. Combined with the long short-term memory (LSTM) model, the prediction of the failure trend of automatic transverse robot during the motion process has been achieved. A prototype digital twin system is developed for validation, which completes the closed-loop system of digital twin theory and proves the feasibility and effectiveness of the proposed system.

Keywords

• Digital twin
• customized automatic transverse robots
• feedback
• LSTM
• failure prediction