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How to Be Sure a Faulty System Does Not Always Appear Healthy?

  • Lina YeEmail author
  • Philippe Dague
  • Delphine Longuet
  • Laura Brandán Briones
  • Agnes Madalinski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11181)

Abstract

Fault diagnosis is a crucial and challenging task in the automatic control of complex systems, whose efficiency depends on the diagnosability property of a system. Diagnosability describes the system property allowing one to determine with certainty whether a given fault has effectively occurred based on the available observations. However, this is a quite strong property that generally requires a high number of sensors. Consequently, it is not rare that developing a diagnosable system is too expensive. In this paper, we analyze a new discrete event system property called manifestability, that represents the weakest requirement on observations for having a chance to identify on line fault occurrences and can be verified at design stage. Intuitively, this property makes sure that a faulty system cannot always appear healthy, i.e., has at least one future behavior after fault occurrence observably distinguishable from all normal behaviors. Then, we prove that manifestability is a weaker property than diagnosability before proposing an algorithm with PSPACE complexity to automatically verify both properties. Furthermore, we prove that the problem of manifestability verification itself is PSPACE-complete. The experimental results show the feasibility of our algorithm from a practical point of view. Finally, we compare our approach with related work.

Keywords

Fault Occurrence Discrete Event Systems (DESs) Stochastic Diagnosability Critical Pairs Infinite Words 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Lina Ye
    • 1
    Email author
  • Philippe Dague
    • 2
  • Delphine Longuet
    • 2
  • Laura Brandán Briones
    • 3
  • Agnes Madalinski
    • 4
  1. 1.LRI, Univ. Paris-Sud, CentraleSupélec, Univ. Paris-SaclayOrsayFrance
  2. 2.LRI, Univ. Paris-Sud, CNRS, Univ. Paris-SaclayOrsayFrance
  3. 3.Universidad Nacional de CórdobaCórdobaArgentina
  4. 4.Otto-von-Guericke-University MagdeburgMagdeburgGermany

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