Diagnosability of automated production systems using Petri net based models

This paper presents a method for studying diagnosability of automated production systems (APS) using Petri nets theory. APS are assumed to be modeled as functional graphs. The method enables a systematic translation of functional graphs into Petri nets called functional nets that preserve their semantic (causal relations). Our method is based on motif recognition and a priority rule between these motifs. The method exploits the functional graph hierarchy to capture motifs between levels in a functional graph. Properties of functional nets are discussed and propositions are given to analyze the nets to obtain information about the diagnosability in functional graphs.