• DocumentCode
    2603132
  • Title

    Polynomial test for Stochastic Diagnosability of discrete event systems

  • Author

    Chen, Jun ; Kumar, Ratnesh

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Iowa State Univ., Ames, IA, USA
  • fYear
    2012
  • fDate
    20-24 Aug. 2012
  • Firstpage
    521
  • Lastpage
    526
  • Abstract
    Two types of diagnosability of stochastic discrete-event systems (DESs) were introduced by Thorsley et al. in 2005, where a necessary and sufficient condition for Strong Stochastic Diagnosability (referred as A-diagnosability in [2]), and a sufficient condition for Stochastic Diagnosability (referred as AA-diagnosability in [2]), both with exponential complexity, were reported. In this paper, we present polynomial complexity tests for checking (i) necessity and sufficiency of Strong Stochastic Diagnosability, (ii) sufficiency of Stochastic Diagnosability for arbitrary DESs, and (iii) necessity as well as sufficiency of Stochastic Diagnosability for a class of DESs that have certain ergodicity property. Thus the work presented improves the accuracy as well as complexity of testing stochastic diagnosability.
  • Keywords
    computational complexity; discrete event systems; failure analysis; fault diagnosis; reliability theory; statistical testing; stochastic systems; AA-diagnosability; arbitrary DES; ergodicity property; exponential complexity; necessary and sufficient condition; necessity and sufficiency checking; polynomial complexity test; stochastic discrete event system diagnosability; strong stochastic diagnosability testing; Automata; Complexity theory; Markov processes; Polynomials; Probabilistic logic; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Automation Science and Engineering (CASE), 2012 IEEE International Conference on
  • Conference_Location
    Seoul
  • ISSN
    2161-8070
  • Print_ISBN
    978-1-4673-0429-0
  • Type

    conf

  • DOI
    10.1109/CoASE.2012.6386477
  • Filename
    6386477