Diagnosability of repairable faults View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-06

AUTHORS

Eric Fabre, Loïc Hélouët, Engel Lefaucheux, Hervé Marchand

ABSTRACT

The diagnosis problem for discrete event systems consists in deciding whether some fault event occurred or not in the system, given partial observations on the run of that system. Diagnosability checks whether a correct diagnosis can be issued in bounded time after a fault, for all faulty runs of that system. This problem appeared two decades ago and numerous facets of it have been explored, mostly for permanent faults. It is known for example that diagnosability of a system can be checked in polynomial time, while the construction of a diagnoser is exponential. The present paper examines the case of transient faults, that can appear and be repaired. Diagnosability in this setting means that the occurrence of a fault should always be detected in bounded time, but also before the fault is repaired, in order to prepare for the detection of the next fault or to take corrective measures while they are needed. Checking this notion of diagnosability is proved to be PSPACE-complete. It is also shown that faults can be reliably counted provided the system is diagnosable for faults and for repairs. More... »

PAGES

183-213

References to SciGraph publications

  • 2008-11. Opacity generalised to transition systems in INTERNATIONAL JOURNAL OF INFORMATION SECURITY
  • 2007-12. Concurrent Secrets in DISCRETE EVENT DYNAMIC SYSTEMS
  • 1992. The emptiness problem for intersections of regular languages in MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE 1992
  • 2003-05-27. Distributed Diagnosis of Discrete-Event Systems Using Petri Nets in APPLICATIONS AND THEORY OF PETRI NETS 2003
  • 2004-04. Diagnosis of Intermittent Faults in DISCRETE EVENT DYNAMIC SYSTEMS
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    URI

    http://scigraph.springernature.com/pub.10.1007/s10626-017-0255-8

    DOI

    http://dx.doi.org/10.1007/s10626-017-0255-8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1092261211


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