Fault identification through the combination of symbolic conflict recognition and Markov Chain-aided belief revision

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dc.contributor.author Shen, Qiang
dc.contributor.author Smith, Finlay
dc.date.accessioned 2008-01-15T20:54:31Z
dc.date.available 2008-01-15T20:54:31Z
dc.date.issued 2004-09
dc.identifier.citation Shen , Q & Smith , F 2004 , ' Fault identification through the combination of symbolic conflict recognition and Markov Chain-aided belief revision ' IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics , vol 34 , no. 5 , pp. 649-663 . en
dc.identifier.other PURE: 74683
dc.identifier.other dspace: 2160/431
dc.identifier.uri http://hdl.handle.net/2160/431
dc.description F. Smith and Q. Shen. Fault identification through the combination of symbolic conflict recognition and Markov Chain-aided belief revision. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 34(5):649-663, 2004. en
dc.description.abstract Fault identification is a search for possible behaviors that would explain the observed behavior of a physical system. During this search, different possible models are considered and information about the interaction between possible behaviors is derived. Much of this potentially useful information is generally ignored in conventional pure symbolic approaches to fault diagnosis, however. A novel approach is presented in this paper that exploits uncertain information on the behavioral description of system components to identify possible fault behaviors in physical systems. The work utilizes the standard conflict recognition technique developed in the framework of the general diagnostic engine (GDE) to support diagnostic inference through the production of both rewarding and penalizing evidence. In particular, Markov matrices are derived from the given evidence, thereby enabling the use of Markov chains to implement the diagnostic process. This work has resulted in a technique, which maximizes the use of derived information, for identifying candidates for multiple faults that is demonstrated to be very effective. en
dc.format.extent 15 en
dc.language.iso eng
dc.relation.ispartof IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics en
dc.title Fault identification through the combination of symbolic conflict recognition and Markov Chain-aided belief revision en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1109/TSMCA.2004.832826
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Peer reviewed en


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