Causal Temporal Signature from diagnoser model for online diagnosis of Discrete Event Systems

Abstract

In Discrete Events System (DES), there are two basic approaches to diagnosis: the first approach is the diagnosers and the second approach is Causal Temporal Signature (CTS) and chronicles. The first approach has limitations including the issue of combinatorial explosion. On the other side, it offers tools to study the diagnosability of the models constructed. CTS are easier to write but pose the problem of the guarantee of the completeness of a given base. This means that there is at least one CTS in the set of CTS for all the faults in the monitored system. This study aims to propose a method to garantee the completeness of a set of CTS. The method is based on a translation of formalism and model of a diagnoser into CTS. From these CTS, a recognition algorithm based on the concept of “world” is used. A “world” is defined as a set of coherent hypotheses of assignment of the event received by the diagnostic task.