Temporal fault diagnosis for k-bounded non-Markovian SPN

This paper concerns the diagnosis of temporal faults for stochastic discrete event systems that behave according to non-Markovian dynamics. K-bounded partially observed Petri nets are used to model the system structure and the sensors. Stochastic processes with probability density functions of finite support model the dynamics. Temporal faults are defined according to time constraints that must be fulfilled by the firing durations. From the proposed modelling and the collected timed measurements, the probabilities of consistent trajectories are computed with a numerical scheme. The advantage of the proposed scheme is that it can be used for a large variety of probability density functions. It works also for various time semantics. Diagnosis in terms of probability is established as a consequence.