Biblio

This paper presents a method to improve the diagnosability of power network under multiple faults. In this paper, the steps of fault diagnosis are as follows: first, constructing finite automata model of the diagnostic system; then, a fault diagnoser model is established through coupling operation and trajectory reasoning mechanism; finally, the diagnosis results are obtained through this model. In this paper, the judgment basis of diagnosability is defined. Then, based on the existing diagnosis results, the information available can be increased by adding sensor devices, to achieve the purpose of diagnosability in the case of multiple faults of the system.

Various aspects of security and privacy in many application domains can be assessed based on proper analysis of successive measurements that are collected on a given system. This work is devoted to such issues in the context of timed stochastic Petri net models. We assume that certain events and part of the marking trajectories are observable to adversaries who aim to determine when the system is performing secret operations, such as time intervals during which the system is executing certain critical sequences of events (as captured, for instance, in language-based opacity formulations). The combined use of the k-step trajectory-observer and the Markov model of the stochastic Petri net leads to probabilistic indicators helpful for evaluating language-based opacity of the given system, related timing aspects, and possible strategies to improve them.