This paper concerns the diagnosis of 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 (pdf) of finite support define the dynamics. Structural and temporal faults are considered. Structural faults correspond to specific sequences of events that should satisfy precedence conditions defined with patterns. Temporal faults are defined with time constraints that must be fulfilled by the firing durations. The probabilities of consistent trajectories are computed with a numerical scheme from the collected timed measurements. The advantage of the proposed scheme is that it can be used for a large variety of pdf that may be defined either with an analytical or a numerical description. It works also for various time semantics. Diagnosis in terms of probability for faulty patterns and temporal constraints is established as a consequence.

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.

This paper deals with problems of detection and isolation of temporal faults in timed stochastic discrete event systems. Partially labeled timed Petri nets are used to model the considered systems. Temporal faults corresponding to significant variations of the support of the probability density function (pdf) are considered. A pdf represents the firing duration of each transition. A Moving Average control chart (also known as a Moving Mean chart) is applied in order to detect the variation of mean duration. The advantages of the proposed analysis are to detect variations in time series when parameters vary slowly and to isolate the faults thanks to the signature table.

The first step in the evaluation of vehicular ad hoc networks (VANETs) applications is based on simulations. The quality of those simulations not only depends on the accuracy of the network model but also on the degree of reality of the underlying mobility model. VehILux-a recently proposed vehicular mobility model, allows generating realistic mobility traces using traffic volume count data. It is based on the concept of probabilistic attraction points. However, this model does not address the question of how to select the best values of the probabilities associated with the points. Moreover, these values depend on the problem instance (i.e. geographical region). In this article we demonstrate how genetic algorithms (GAs) can be used to discover these probabilities. Our approach combined together with VehILux and a traffic simulator allows to generate realistic vehicular mobility traces for any region, for which traffic volume counts are available. The process of the discovery of the probabilities is represented as an optimisation problem. Three GAs-generational GA, steady-state GA, and cellular GA-are compared. Computational experiments demonstrate that using basic evolutionary heuristics for optimising VehILux parameters on a given problem instance permits to improve the model realism. However, in some cases, the results significantly deviate from real traffic count data. This is due to the route generation method of the VehILux model, which does not take into account specific behaviour of drivers in rush hours.