Reverse twin plant for efficient diagnosability testing and optimizing

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

Reverse twin plant for efficient diagnosability testing and optimizing

Li, B Guo, T

Zhu, X Li, Z

Permalink

Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Engineering Applications of Artificial Intelligence, 2015, 38, pp. 131-137
Issue Date:: 2015-02-01

Closed Access

	Filename	Description	Size
	Reverse twin plant for efficient diagnosability testing and optimizing.pdf	Published version	588.81 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Li, B
dc.contributor.author	Guo, T https://orcid.org/0000-0001-5130-3237
dc.contributor.author	Zhu, X
dc.contributor.author	Li, Z
dc.date.accessioned	2023-03-28T21:32:01Z
dc.date.available	2023-03-28T21:32:01Z
dc.date.issued	2015-02-01
dc.identifier.citation	Engineering Applications of Artificial Intelligence, 2015, 38, pp. 131-137
dc.identifier.issn	0952-1976
dc.identifier.issn	1873-6769
dc.identifier.uri	http://hdl.handle.net/10453/168756
dc.description.abstract	Model-based diagnosis in discrete event systems (DESs) is a major research topic in failure diagnosis, where diagnosability plays an important role in the construction of the diagnosis engine. To improve the solution efficiency for diagnosability, this paper proposes novel techniques to solve the problems of testing and optimizing for diagnosability. We propose a new concept, reverse twin plant, which is generated backwards from the final states of the DESs so there is no need to generate a complete copy of the DES model to determine the diagnosability. Such a design makes our testing algorithm much faster than existing methods. An efficient optimizing algorithm, which makes a non-diagnosable system diagnosable, is also proposed in the paper by expanding the minimal observable space with operation on just a part of the DES model. Examples and theoretical studies demonstrate the performance of the proposed designs.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Engineering Applications of Artificial Intelligence
dc.relation.isbasedon	10.1016/j.engappai.2014.10.007
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Reverse twin plant for efficient diagnosability testing and optimizing
dc.type	Journal Article
utslib.citation.volume	38
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-28T21:31:59Z
pubs.publication-status	Published
pubs.volume	38

Abstract:

Model-based diagnosis in discrete event systems (DESs) is a major research topic in failure diagnosis, where diagnosability plays an important role in the construction of the diagnosis engine. To improve the solution efficiency for diagnosability, this paper proposes novel techniques to solve the problems of testing and optimizing for diagnosability. We propose a new concept, reverse twin plant, which is generated backwards from the final states of the DESs so there is no need to generate a complete copy of the DES model to determine the diagnosability. Such a design makes our testing algorithm much faster than existing methods. An efficient optimizing algorithm, which makes a non-diagnosable system diagnosable, is also proposed in the paper by expanding the minimal observable space with operation on just a part of the DES model. Examples and theoretical studies demonstrate the performance of the proposed designs.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/168756