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Diagnosability analysis for self-observed distributed discrete event systems

Abstract : Diagnosability is a crucial property that determines at design stage how accurate any diagnosis algorithm can be on a partially observable system and thus has a significant impact on the performance and reliability of complex systems. Most existing approaches assumed that observable events in the system are globally observed. But sometimes it is not possible to obtain global information. Thus a recent work has proposed a new framework to check diagnosability in a system where each component can only observe its own observable events to keep the internal structure private in terms of observations. However, the authors implicitly assume that local paths in components can be exhaustively enumerated, which is not true in a general case where there are embedded cycles. In this paper, we get some new results about diagnosability in such a system, i.e., what we call joint diagnosability in a self-observed distributed system. First we prove the undecidability of joint diagnosability with unobservable communication events by reducing Post's Correspondence Problem to an observation problem. Then we propose an algorithm to check a sufficient but not necessary condition of joint diagnosability. Finally we briefly discuss about the decidable case with observable communication events.
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Submitted on : Tuesday, February 19, 2013 - 2:45:51 PM
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  • HAL Id : hal-00790135, version 1



Lina Ye, Philippe Dague. Diagnosability analysis for self-observed distributed discrete event systems. VALID - 4th International Conference on Advances in System Testing and Validation Lifecycle, Nov 2012, Lisbonne, Portugal. ⟨hal-00790135⟩



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