A Petri-net approach to the control of discrete-event systems
Proceedings of the NATO Advanced Study Institute on The Application of Advanced Computing Concepts and Techniques in Control Engineering on Advanced computing concepts and techniques in control engineering
Performance modeling of automated manufacturing systems
Performance modeling of automated manufacturing systems
Petri Nets and Grafcet: Tools for Modelling Discrete Event Systems
Petri Nets and Grafcet: Tools for Modelling Discrete Event Systems
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Coordinated Decentralized Protocols for Failure Diagnosisof Discrete Event Systems
Discrete Event Dynamic Systems
Centralized Modular Diagnosis and the Phenomenon of Coupling
WODES '02 Proceedings of the Sixth International Workshop on Discrete Event Systems (WODES'02)
Distributed Diagnosis for Qualitative Systems
WODES '02 Proceedings of the Sixth International Workshop on Discrete Event Systems (WODES'02)
Distributed diagnosis of discrete-event systems using Petri nets
ICATPN'03 Proceedings of the 24th international conference on Applications and theory of Petri nets
Diagnosis of a class of distributed discrete-event systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Brief paper: Abstraction-based verification of codiagnosability for discrete event systems
Automatica (Journal of IFAC)
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An awareness of failure type and location is an indispensable requirement for the establishment of adequate recovery strategies and the maintenance of Factory Automation and Process Control systems.The failure diagnosis methodology presented in this paper is based on Discrete Event Systems models and in the diagnoser concept, which enable the off-line and on-line analysis of systems failures. We present an approach for models and associated diagnosers based on a modular decomposition of the global system, with the aim of avoiding problems of exponential explosion in the number of states and computational complexity of the diagnosis process.