Faults prognosis using partially observed stochastic Petri nets
Rabah Ammour, Edouard Leclercq, Éric Sanlaville, Dimitri Lefebvre · 2016
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. Marking trajectories which are consistent with the measurements issued from the sensors are first obtained. Based on the events 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 fault is developed using a probabilistic model. An example is presented to illustrate the results.