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