• DocumentCode
    14345
  • Title

    Polynomial Test for Stochastic Diagnosability of Discrete-Event Systems

  • Author

    Jun Chen ; Kumar, Ravindra

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Iowa State Univ., Ames, IA, USA
  • Volume
    10
  • Issue
    4
  • fYear
    2013
  • fDate
    Oct. 2013
  • Firstpage
    969
  • Lastpage
    979
  • Abstract
    Two types of diagnosability of stochastic discrete-event systems (DESs) were introduced by Thorsley in 2005, where a necessary and sufficient condition for Strong Stochastic (SS)-Diagnosability (referred as A-diagnosability by Thorsley and Teneketzis, 2005), and a sufficient condition for Stochastic (S)-Diagnosability (referred as AA-diagnosability by Thorsley and Teneketzis, 2005), both with exponential complexity, were reported. In this paper, we present polynomial complexity tests for checking: (i) necessity and sufficiency of SS-Diagnosability; (ii) sufficiency of S-Diagnosability; and (iii) sufficiency as well as necessity of S-Diagnosability; the latter requires an additional notion of probabilistic equivalence. Thus, the work presented improves the accuracy as well as the complexity of verifying stochastic diagnosability.
  • Keywords
    discrete event systems; fault diagnosis; polynomials; stochastic processes; exponential complexity; polynomial complexity testing; probabilistic equivalence; stochastic diagnosability; stochastic discrete event systems; Complexity theory; Discrete-event systems; Markov processes; Polynomials; Stochastic processes; Complexity; Stochastic diagnosability; discrete-event system (DES); hypothesis testing;
  • fLanguage
    English
  • Journal_Title
    Automation Science and Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1545-5955
  • Type

    jour

  • DOI
    10.1109/TASE.2013.2251334
  • Filename
    6496164