Discovering Petri Net Models of Discrete-Event Processes by Computing T-Invariants

Tonatiuh Tapia-Flores (1) , Ernesto López-Mellado (1) , Ana Paula Estrada-Vargas (2) , Jean-Jacques Lesage (3)

1 CINVESTAV - Centro de Investigacion y de Estudios Avanzados del Instituto Politécnico Nacional
2 Oracle de México
3 LURPA - Laboratoire Universitaire de Recherche en Production Automatisée

This paper addresses the problem of discovering a Petri Net (PN) from a long event sequence representing the behavior of discrete-event processes. A method for building a 1-bounded PN able to execute the events sequence S is presented; it is based on determining causality and concurrence relations between events and computing the t-invariants. This novel method determines the structure and the initial marking of an ordinary PN, which reproduces the behavior in S. The algorithms derived from the method are efficient and have been implemented and tested on numerous examples of diverse complexity. Note to Practitioners—Model discovery is useful to perform reverse engineering of ill-known systems. The algorithms proposed in this paper build 1-bounded PN models, which are enough powerful to describe many discrete-event processes from industry. The efficiency of the method allows processing very large sequences. Thus, an automated modeling tool can be developed for dealing with data issued from real systems.

Mots clés: Model discovery; Petri nets (PNs); t-invariants

Domaines: Automatique / Robotique

Tonatiuh Tapia-Flores, Ernesto López-Mellado, Ana Paula Estrada-Vargas, Jean-Jacques Lesage. Discovering Petri Net Models of Discrete-Event Processes by Computing T-Invariants. IEEE Transactions on Automation Science and Engineering, 2018, 15 (3), pp. 992-1003. ⟨10.1109/TASE.2017.2682060⟩. ⟨hal-01526076⟩