Coordinated Decentralized Protocols for Failure Diagnosisof Discrete Event Systems
Discrete Event Dynamic Systems
Fault Diagnosis for Timed Automata
FTRTFT '02 Proceedings of the 7th International Symposium on Formal Techniques in Real-Time and Fault-Tolerant Systems: Co-sponsored by IFIP WG 2.2
Diagnosability of Discrete Event Systems with Modular Structure
Discrete Event Dynamic Systems
Diagnosis of Discrete Event Systems Using Decentralized Architectures
Discrete Event Dynamic Systems
Robust codiagnosability of discrete event systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
Introduction to Discrete Event Systems
Introduction to Discrete Event Systems
Decentralized failure diagnosis of discrete event systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Robust diagnosis of discrete-event systems against permanent loss of observations
Automatica (Journal of IFAC)
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In the usual approaches to fault diagnosis of discrete event systems it is assumed that not only all sensors work properly but also all information reported by sensors always reaches the diagnoser. Any bad sensor operation or communication failure between sensors and the diagnoser can be regarded as loss of observations of events initially assumed as observable. In such situations, it may be possible that either the diagnoser stands still or report some wrong information regarding the fault occurrence. In this paper we assume that intermittent loss of observations may occur and we propose an automaton model based on a new language operation (language dilation) that takes it into account. We refer to this problem as robust diagnosability against intermittent loss of observations (or simply robust diagnosability, where the context allows). We present a necessary and sufficient condition for robust diagnosability in terms of the language generated by the original automaton and propose two tests for robust language diagnosability, one that deploys diagnosers and another one that uses verifiers. We also extend the results to robust codiagnosability against intermittent loss of observations.