A Black-Box Identification Method for Automated Discrete-Event Systems

This paper deals with the identification of discrete-event manufacturing systems that are automated using a programmable logic controller (PLC). The behavior of the closed-loop system (PLC and Plant) is observed during its operation and is represented by a single long sequence of observed input/output (I/O) signals vectors. The proposed method follows a black-box and passive identification approach that allows addressing large and complex industrial DES and yields compact and expressive interpreted Petri net (IPN) models. It consists of two complementary stages; the first one obtains, from the I/O sequence, the reactive part of the model composed by observable places and transitions. The I/O sequence is also mapped into a sequence of the created transitions, from which the second stage builds the non observable part of the model including places that ensure the reproduction of the observed input output sequence. This method, based on polynomial-time algorithms on the size of the input data, has been implemented as a software tool that generates and draws the IPN model; it has been tested with input/output sequences obtained from real systems in operation. The tool is described and its application is illustrated through a case study.