DocumentCode
2036255
Title
A Petri net approach to fault detection and diagnosis in distributed systems. II. Extending Viterbi algorithm and HMM techniques to Petri nets
Author
Aghasaryan, A. ; Fabre, E. ; Benveniste, A. ; Boubour, R. ; Jard, C.
Author_Institution
IRISA, Rennes, France
Volume
1
fYear
1997
fDate
10-12 Dec 1997
Firstpage
726
Abstract
For pt.I see ibid., p.720-5 (1997). We present an original construction of stochastic Petri nets (PN) dedicated to large distributed discrete event systems. Its main characteristic is to provide statistically independent behaviors to concurrent (parallel) processes of the system. We end up with “hybrid” model where only some events are randomized, and that can´t be described by a standard Markov dynamics. Equivalently, time is only partially ordered in such systems. Then assuming that every fired transition produces a random label we address the problem of finding the most likely path in the net, given a sequence of such labels. This problem is usually solved by dynamic programming on the state-space (marking graph of the PN). The proposed approach instead is based the net unfolding
Keywords
Petri nets; Viterbi detection; discrete event systems; dynamic programming; fault diagnosis; hidden Markov models; state-space methods; HMM techniques; Markov dynamics; Viterbi algorithm; concurrent processes; distributed systems; dynamic programming; fault detection; fault diagnosis; large distributed discrete event systems; marking graph; parallel processes; partially ordered time; random label; statistically independent behaviors; stochastic Petri nets; Discrete event systems; Fault detection; Fault diagnosis; History; Maximum likelihood estimation; Petri nets; Stochastic processes; Stochastic systems; Telecommunications; Viterbi algorithm;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1997., Proceedings of the 36th IEEE Conference on
Conference_Location
San Diego, CA
ISSN
0191-2216
Print_ISBN
0-7803-4187-2
Type
conf
DOI
10.1109/CDC.1997.650721
Filename
650721
Link To Document