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
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