Online Fault Diagnoser for Partially Observed Petri Nets

Abstract

This paper investigates the fault detection problem for Discrete Event Systems which can be modeled by Partially Observed Petri Nets (POPN). To overcome the shortage of the low diagnosability of the currently POPN online fault diagnoser, we propose an improved on-line fault diagnosis algorithm that integrates Generalized Mutual Exclusion Constraints(GMEC) and Integer Linear Programming (ILP). We assume that the POPN structure and its initial markings are known, the faults are modeled as unobservable transitions. First, the event sequence is observed and recorded. The ILP problem of POPN is solved for elementary diagnosis for the system behavior. While the system is diagnosed that some faults may have happened, we use the GMEC for the further diagnosis. Finally, a real discrete event system is taken as an example, we model and analyse the discrete event system, the proposed algorithm increases the diagnosablity remarkably.