Faulty patterns diagnosis for k-bounded non-Markovian timed stochastic Petri nets

Abstract

This paper concerns the diagnosis of stochastic discrete event systems that behave with 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 are used to model the dynamics including some failure processes. The faults to be detected and isolated are defined as faulty patterns. From the proposed modelling and the timed measurements, the probabilities of consistent trajectories are computed with a numerical scheme. Diagnosis in terms of probability is established as a consequence. The advantage of the proposed scheme is that it can be used for arbitrary probability density functions. It works also for various time semantics including race and preselection policies. Consequently it is suitable in many application domains including manufacturing, computer science, transport and logistic.