Diagnosis and Diagnosability of Discrete Event Systems using Petri Nets

Abstract

Fault diagnosis in dynamic systems is a subject that has received a lot of attention in the past decades: the objective is that of understanding if a system is affected by a fault, and if so to identify the fault. In the context of discrete event systems faults are modeled by unobservable (or undistinguishable) events whose firing cannot be directly detected but must be reconstructed on the basis of the observed evolution. This implies that the approaches for diagnosis are strictly related to the problems of designing an observer for state and event sequence estimation.Diagnosability analysis aims to determine if a system is diagnosable, i.e., if the occurrence of fault can be identified in a finite number of steps. Both diagnosis and diagnosability have been mostly studied in the context of finite state automata, and the complexity of solving these problems is often unmanageable due to the state space explosion.This talk focus on a particular class of discrete event models, Petri nets. The goal is to show that the observer design can be efficiently addressed using Petri net models and can naturally be extended to diagnosis and diagnosability.