Partial Order Techniques for Distributed Discrete Event Systems: Why You Cannot Avoid Using Them
Discrete Event Dynamic Systems
Diagnosability of fuzzy discrete event systems
Information Sciences: an International Journal
Robust codiagnosability of discrete event systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
Brief paper: Abstraction-based verification of codiagnosability for discrete event systems
Automatica (Journal of IFAC)
The complexity of codiagnosability for discrete event and timed systems
ATVA'10 Proceedings of the 8th international conference on Automated technology for verification and analysis
Context-sensitive diagnosis of discrete-event systems
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Verification of robust diagnosability for partially observed discrete event systems
Automatica (Journal of IFAC)
Robust diagnosis of discrete event systems against intermittent loss of observations
Automatica (Journal of IFAC)
Robust diagnosis of discrete-event systems against permanent loss of observations
Automatica (Journal of IFAC)
Autonomous, failure-resilient orchestration of distributed discrete event simulations
Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference
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By decentralized diagnosis we mean diagnosis using multiple diagnosers, each possessing its own set of sensors, without involving any communication among diagnosers or to any coordinators. The notion of decentralized diagnosis is formalized by introducing the notion of codiagnosability that requires that a failure be detected by one of the diagnosers within a bounded delay. Algorithms of complexity polynomial in the size of the system and the nonfault specification are provided for: 1) testing codiagnosability, 2) computing the bound in delay of diagnosis, 3) offline synthesis of individual diagnosers, and 4) online diagnosis using them. The notion of codiagnosability and the above algorithms are initially presented in a setting of a specification language (violation of which represents a fault) and are later specialized to the case where faults are modeled as the occurrences of certain events. The notion of strong codiagnosability is also introduced to capture the ability of being certain about both the failure as well as the nonfailure conditions in a system within a bounded delay.