Artificial Intelligence Data-Driven Petri nets Approach for Virtualizing Digital Twins
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abstract
This work has been supported by the Foundation for Science
and Technology (FCT, Portugal) through national funds
FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and
UIDP/05757/2020) and SusTEC (LA/P/0007/2021).
Virtualization is one key design principle in Industry
4.0, with the modeling and simulation of the physical assets
playing crucial roles in the Digital Twin context. Different
approaches can be used to implement the virtual asset models,
ranging from simple equations to complex mathematical models.
Petri nets formalism is a suitable approach to model and
simulate the physical asset operation in the Digital context,
particularly those that are event-driven, taking advantage of its
inherent robust mathematical foundation. Having this in mind,
this paper proposes a Petri nets approach, which considers
Artificial Intelligent data-driven analytics associated to timed
transitions to support the execution of what-if simulation aiming
the monitoring, diagnosis, prediction, and optimization. The proposed
approach was tested in an experimental punching machine,
allowing the early identification the performance degradation in
the Digital Twin and the selection of actions to be implemented
in the physical asset to improve its operation.
