Model-based diagnosis of discrete event systems

In this paper we propose an online model-based probabilistic diagnosis algorithm for Petri nets. The plant observation is given by a subset of transitions whose occurrence is reported while the rest of transitions are unobservable silent. The faults are represented by a subset of unobservable transitions. We propose the probabilistic detection of a fault given the currently available observations. A tool PNDesigner has been designed to analyse Petri nets, to design supervisory controllers, and to test the proposed fault algorithms