Basis Coverability Graph for Partially Observable Petri Nets with Application to Diagnosability Analysis View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2018-05-08

AUTHORS

Engel Lefaucheux , Alessandro Giua , Carla Seatzu

ABSTRACT

Petri nets have been proposed as a fundamental model for discrete-event systems in a wide variety of applications and have been an asset to reduce the computational complexity involved in solving a series of problems, such as control, state estimation, fault diagnosis, etc. Many of those problems require an analysis of the reachability graph of the Petri net. The basis reachability graph is a condensed version of the reachability graph that was introduced to efficiently solve problems linked to partial observation. It was in particular used for diagnosis which consists in deciding whether some fault events occurred or not in the system, given partial observations on the run of the system. However this method is, with very specific exceptions, limited to bounded Petri nets. In this paper, we introduce the notion of basis coverability graph to remove this requirement. We then establish the relationship between the coverability graph and the basis coverability graph. Finally, we focus on the diagnosability problem: we show how the basis coverability graph can be used to get an efficient algorithm. More... »

PAGES

164-183

References to SciGraph publications

  • 2017. The Complexity of Diagnosability and Opacity Verification for Petri Nets in APPLICATION AND THEORY OF PETRI NETS AND CONCURRENCY
  • 1998. The state explosion problem in LECTURES ON PETRI NETS I: BASIC MODELS
  • 2013. Introduction to Petri Nets in CONTROL OF DISCRETE-EVENT SYSTEMS
  • 1996. Partial-Order Methods for the Verification of Concurrent Systems, An Approach to the State-Explosion Problem in NONE
  • Book

    TITLE

    Application and Theory of Petri Nets and Concurrency

    ISBN

    978-3-319-91267-7
    978-3-319-91268-4

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-91268-4_9

    DOI

    http://dx.doi.org/10.1007/978-3-319-91268-4_9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1103859776


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