Online Fault Diagnosis in Discrete Event Systems with Partially Observed Petri Nets

This paper investigates the fault detection problem for Discrete Event Systems (DES) which can be modeled by Partially Observed Petri Nets (POPN). To overcome the problem of low diagnosability in the POPN online fault diagnoser in current use, we propose an improved online 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, and the faults are modeled as unobservable transitions. First, the event sequence is observed and recorded. We use GMEC for elementary diagnosis of the system behavior,then the ILP problem of POPN is solved for further diagnosis. Finally, we modeled and analyzed an example of a real DES to test the new fault diagnoser. The proposed algorithm increased the diagnosability of the DES remarkably, and the effectiveness of the new algorithm integrating GMEC and ILP was verified.