As manufacturing rapidly evolves, optimizing processes is essential. Digital Twins, which act as near real-time virtual replicas of the corresponding real-world systems, can support this optimization by providing insights and supporting decision-making. Digital Twins can only be fully effective if their underlying models continuously and accurately reflect the corresponding physical systems. However, not all model components change at the same pace, and relevant data updates also vary in frequency. Thus, Digital Twins require robust validation mechanisms that can identify what parts of models need to be re-extracted, what parts need to be recalibrated, and what parts need to remain same. This is a complex task that necessitates precise partitioning of models with respect to the above noted considerations. Here, we propose a novel approach to modular validation, aimed at supporting Digital Twins. To illustrate our approach, we provide a case study in reliability analysis of manufacturing systems.

As manufacturing rapidly evolves, optimizing processes is essential. Digital Twins, which act as near real-time virtual replicas of the corresponding real-world systems, can support this optimization by providing insights and supporting decision-making. Digital Twins can only be fully effective if their underlying models continuously and accurately reflect the corresponding physical systems. However, not all model components change at the same pace, and relevant data updates also vary in frequency. Thus, Digital Twins require robust validation mechanisms that can identify what parts of models need to be re-extracted, what parts need to be recalibrated, and what parts need to remain same. This is a complex task that necessitates precise partitioning of models with respect to the above noted considerations. Here, we propose a novel approach to modular validation, aimed at supporting Digital Twins. To illustrate our approach, we provide a case study in reliability analysis of manufacturing systems.