Faults prognosis using partially observed stochastic Petri-nets: an incremental approach

This article deals with the problem of fault prognosis in stochastic discrete event systems. For that purpose, partially observed stochastic Petri nets are considered to model the system with its sensors. The model represents both healthy and faulty behaviors of the system. Our goal is, based on a timed measurement trajectory issued from the sensors, to compute the probability that a fault will occur in a future time interval. To this end, a procedure based on an incremental algorithm is proposed to compute the set of consistent behaviors of the system. Based on the measurement dates, the probabilities of the consistent trajectories are evaluated and a state estimation is obtained as a consequence. From the set of possible current states and their probabilities, a method to evaluate the probability of future faults is developed using a probabilistic model. An example is presented to illustrate the results.