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
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;
Journal_Title :
Automation Science and Engineering, IEEE Transactions on
DOI :
10.1109/TASE.2013.2251334