Large scale diagnosis using associations between system outputs and components

Guo, T; Li, Z; Guo, R; Zhu, X

Large scale diagnosis using associations between system outputs and components

Guo, T

Li, Z Guo, R Zhu, X

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the National Conference on Artificial Intelligence, 2011, 2 pp. 1786 - 1787
Issue Date:: 2011-11-02

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Field	Value	Language
dc.contributor.author	Guo, T https://orcid.org/0000-0001-5130-3237	en_US
dc.contributor.author	Li, Z	en_US
dc.contributor.author	Guo, R	en_US
dc.contributor.author	Zhu, X	en_US
dc.date.issued	2011-11-02	en_US
dc.identifier.citation	Proceedings of the National Conference on Artificial Intelligence, 2011, 2 pp. 1786 - 1787	en_US
dc.identifier.isbn	9781577355090	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30879
dc.description.abstract	Model-based diagnosis (MBD) uses an abstraction of system to diagnose possible faulty functions of an underlying system. To improve the solution efficiency for multi-fault diagnosis problems, especially for large scale systems, this paper proposes a method to induce reasonable diagnosis solutions, under coarse diagnosis, by using the relationships between system outputs and components. Compared to existing diagnosis methods, the proposed framework only needs to consider associations between outputs and components by using an assumption-based truth maintenance system (ATMS) [de Kleer 1986] to obtain correlation components for every output node. As a result, our method significantly reduces the number of variables required for model diagnosis, which makes it suitable for large scale circuit systems. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.	en_US
dc.relation.ispartof	Proceedings of the National Conference on Artificial Intelligence	en_US
dc.title	Large scale diagnosis using associations between system outputs and components	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2	en_US
dc.location.activity	San Francisco, USA
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

Abstract:

Model-based diagnosis (MBD) uses an abstraction of system to diagnose possible faulty functions of an underlying system. To improve the solution efficiency for multi-fault diagnosis problems, especially for large scale systems, this paper proposes a method to induce reasonable diagnosis solutions, under coarse diagnosis, by using the relationships between system outputs and components. Compared to existing diagnosis methods, the proposed framework only needs to consider associations between outputs and components by using an assumption-based truth maintenance system (ATMS) [de Kleer 1986] to obtain correlation components for every output node. As a result, our method significantly reduces the number of variables required for model diagnosis, which makes it suitable for large scale circuit systems. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

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http://hdl.handle.net/10453/30879