Automated and Systematic Digital Twins Testing for Industrial Processes

TL;DR

This paper presents an automated, systematic testing architecture for digital twins in industrial processes, enhancing their reliability and fidelity through real-time sensor data correlation and continuous performance monitoring.

Contribution

It introduces a novel online testing method and architecture that automate and improve the reliability of digital twins in industrial settings.

Findings

Significantly accelerates DT testing process

Detects performance shifts effectively

Improves DT fidelity through continuous testing

Abstract

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.