Probability of current state and future faults with partially observed stochastic Petri nets

This article concerns state estimation and fault prediction for stochastic discrete event systems. For this purpose, partially observed stochastic Petri nets are introduced that include the sensors used to measure events and markings and the Markovian stochastic dynamics used to represent failure processes. Timed observation sequences result from this modeling and the probabilities of marking trajectories consistent with a given timed observation sequence are systematically computed. State estimation and fault prediction in terms of probability are obtained as a consequence.