Decentralized Failure Diagnosis of Stochastic Discrete Event Systems

TL;DR

This paper introduces a decentralized approach for failure diagnosis in stochastic discrete event systems, allowing multiple local diagnosers to detect failures collaboratively, with formal conditions and computational methods provided.

Contribution

It formalizes codiagnosability for SDESs, constructs a codiagnoser for multiple projections, and offers necessary and sufficient conditions for decentralized failure detection.

Findings

Codiagnosability can be tested using the constructed codiador.

Necessary and sufficient conditions for codiagnosability are established.

A detailed computational method for checking codiagnosability violations is provided.

Abstract

Recently, the diagnosability of {\it stochastic discrete event systems} (SDESs) was investigated in the literature, and, the failure diagnosis considered was {\it centralized}. In this paper, we propose an approach to {\it decentralized} failure diagnosis of SDESs, where the stochastic system uses multiple local diagnosers to detect failures and each local diagnoser possesses its own information. In a way, the centralized failure diagnosis of SDESs can be viewed as a special case of the decentralized failure diagnosis presented in this paper with only one projection. The main contributions are as follows: (1) We formalize the notion of codiagnosability for stochastic automata, which means that a failure can be detected by at least one local stochastic diagnoser within a finite delay. (2) We construct a codiagnoser from a given stochastic automaton with multiple projections, and the codiagnoser associated with the local diagnosers is used to test codiagnosability condition of SDESs. (3) We deal with a number of basic properties of the codiagnoser. In particular, a necessary and sufficient condition for the codiagnosability of SDESs is presented. (4) We give a computing method in detail to check whether codiagnosability is violated. And (5) some examples are described to illustrate the applications of the codiagnosability and its computing method.