Artificial intelligence data-driven petri nets approach for virtualizing digital twins

Oliveira Júnior, Alexandre de; Calvo-Rolle, Jose Luis; Leitão, Paulo

http://hdl.handle.net/10198/29146

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Authors

Oliveira Júnior, Alexandre de

Calvo-Rolle, Jose Luis

Leitão, Paulo

Abstract(s)

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.

Keywords

Digital twin Petri nets Modeling Simulation

URI

http://hdl.handle.net/10198/29146

Citation

Oliveira Júnior, Alexandre de; Calvo-Rolle, Jose Luis; Leitão, Paulo (2023). Artificial intelligence data-driven petri nets approach for virtualizing digital twins. In 2023 IEEE International Conference on Industrial Technology (ICIT). 04-06 April 2023, Orlando. ISSN 2643-2978. p. 1-6