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
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;
Conference_Titel :
Automation Science and Engineering (CASE), 2012 IEEE International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4673-0429-0
DOI :
10.1109/CoASE.2012.6386477