Frequency-Based Partitioning for Modular Validation of Stochastic Petri Net Digital Twin Models

As technological advancements enable the manufacturing industry's adoption of cyber-physical production systems, the critical need for Digital Twins has also become more apparent. They allow for optimization, flexibility, and continuous improvement by replicating physical systems in a digital environment. However, they can only be utilized if their underlying models remain a valid representation of the corresponding real-world systems. As manufacturing systems and their Digital Twins grow in complexity, model validation and decomposition become increasingly challenging, particularly due to heterogeneous system dynamics. In complex manufacturing systems, different events occur at different rates, and a uniform validation approach of the corresponding Digital Twin models may fail to account for these dynamic behaviors. Depending on the rate of occurrence of different events, validation mechanisms must decide whether to preserve, recalibrate, or re-extract different parts of a model. To address this challenge, a modular validation framework was introduced, partitioning Digital Twin models into sub-models and dynamically adjusting validation policies. The effectiveness of such a framework, however, critically depends on how the model is partitioned and whether the partitions capture heterogeneous system behavior while preserving dependencies. In this paper, we present a data-driven, behavior-based approach for partitioning stochastic Petri net Digital Twin models that automatically identifies meaningful partition boundaries by analyzing reachable marking patterns and transition firing frequencies. The approach preserves inter-partition dependencies while enabling independent validation of sub-models. We demonstrate the method through a reliability-focused manufacturing case study, showing that frequency-based partitioning can capture heterogeneous dynamics while preserving system behavior.