Digital-Twin Consistency Checking Based on Observed Timed Events With Unobservable Transitions in Smart Manufacturing

Smart factories manage digital twins (DTs) to evaluate the performance of various what-if production scenarios. This article presents a DT consistency-checking approach to maintain DT in high fidelity by checking whether each sensed timed event from the physical manufacturing plant is under its corresponding DT-based estimations in runtime. The approach targets DTs developed using time colored Petri net (TCPN). To build the candidates of the next observable event with observable time margins, we considered the stochastic property of the plant, frequent external actuation caused by a new order, machine maintenance, etc., as well as intermediate unobservable state transitions reaching the sensible events. Based on the considerations, we propose an iterative method to build the virtual estimates for streaming physical events using efficiently evolved state-class graphs (SCGs). We also propose a TCPN partitioning method to accelerate the SCG-evolution and make DT maintenance easier by supporting the isolation of inconsistent subnets being diagnosed. We applied the approach to a USB flash-drive factory to prove the concept and evaluated the performance under various situations to show speedups of the SCG evolution, that is the crucial overhead of the estimation.

Automated summary

Smart factories use digital twins to evaluate different production scenarios. This article presents a consistency-checking approach to maintain high-fidelity digital twins by comparing sensed timed events from the physical manufacturing plant with DT-based estimations. The approach targets DTs developed using time colored Petri net (TCPN) and includes methods for building virtual estimates and accelerating SCG evolution.