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Diagnosability Analysis for Self-observed Distributed Discrete Event Systems

Authors:
Lina Ye
Philippe Dague

Keywords: diagnosis; distributed diagnosability; finite state machine

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.

Pages: 93 to 98

Copyright: Copyright (c) IARIA, 2012

Publication date: November 18, 2012

Published in: conference

ISSN: 2308-4316

ISBN: 978-1-61208-233-2

Location: Lisbon, Portugal

Dates: from November 18, 2012 to November 23, 2012