Digital twins (DT) of industrial processes have become increasingly important. They aim to digitally represent the physical world to help evaluate, optimize, and predict physical processes and behaviors. Therefore, DT is a vital tool to improve production automation through digitalization and becomes more sophisticated due to rapidly evolving simulation and modeling capabilities, integration of IoT sensors with DT, and high-capacity cloud/edge computing infrastructure. However, the fidelity and reliability of DT software are essential to represent the physical world. This paper shows an automated and systematic test architecture for DT that correlates DT states with real-time sensor data from a production line in the forging industry. Our evaluation shows that the architecture can significantly accelerate the automatic DT testing process and improve its reliability. A systematic online DT testing method can significantly detect the performance shift and continuously improve the DT's fidelity. The snapshot creation methodology and testing agent architecture can be an inspiration and can be generally applicable to other industrial processes that use DT to generalize their automated testing.

翻译：工业流程的数字孪生（DT）正变得日益重要。其目标是对物理世界进行数字化表征，以协助评估、优化和预测物理过程与行为。因此，DT是借助数字化提升生产自动化水平的关键工具，并且随着仿真与建模能力的快速发展、物联网传感器与DT的集成以及高容量云/边缘计算基础设施的完善，其复杂性日益提高。然而，DT软件保真度与可靠性对于准确表征物理世界至关重要。本文提出一种自动化系统化的DT测试架构，该架构将DT状态与来自锻造行业生产线的实时传感器数据相关联。评估结果表明，该架构能够显著加速DT自动测试进程并提升其可靠性。一种系统化的在线DT测试方法可有效检测性能漂移，并持续改善DT的保真度。快照创建方法与测试代理架构可作为参考，并普遍适用于其他使用DT进行自动化测试的工业流程。