Detection of Temporal Anomalies for Partially Observed Timed PNs

Abstract

This article concerns faults detection and isolation for timed stochastic discrete event systems modeled with partially observed timed Petri nets. Events occur according to arbitrary probability density functions. The models include the sensors used to measure events and markings and also the temporal constraints to be satisfied by the system operations. These temporal constraints are defined according to tolerance intervals specified for each transition. A fault is an operation that ends too early or too late. The set of trajectories consistent with a given timed measured trajectory is first computed. Then, the probability that the temporal specifications are unsatisfied is estimated for any sequence of measurements and the probability that a temporal fault has occurred is obtained as a consequence.

FMS-Workflow Modeling Based on P-Timed Stochastic Petri Net

“…That is to say that the first allows a better study of the behavior while it is preferable to use the second for the analysis of the properties of the model. Moreover, our proposed extension of P-timed SPN orientation [43] [44] [45] is based on the assumption that all model transitions must be fired. Therefore, the arrival times of the tokens in the places that are synchronization transition entry places must be compatible.…”