Online support vector machine application for model based fault detection and isolation of HVAC system

Dehestani, D; Eftekhari, F; Guo, Y; Ling, SS; Su, S; Nguyen, HT

Online support vector machine application for model based fault detection and isolation of HVAC system

Dehestani, D Eftekhari, F Guo, Y Ling, SS

Su, S

Nguyen, HT

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Publisher:: IACSIT Press
Publication Type:: Journal Article
Citation:: International Journal of Machine Learning and Computing, 2011, 1 (1), pp. 66 - 72 (7)
Issue Date:: 2011-04-01

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Field	Value	Language
dc.contributor.author	Dehestani, D	en_US
dc.contributor.author	Eftekhari, F	en_US
dc.contributor.author	Guo, Y	en_US
dc.contributor.author	Ling, SS https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Su, S https://orcid.org/0000-0002-5720-8852	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2011-04-01	en_US
dc.identifier.citation	International Journal of Machine Learning and Computing, 2011, 1 (1), pp. 66 - 72 (7)	en_US
dc.identifier.issn	2010-3700	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33168
dc.description.abstract	Abstract—Preventive maintenance plays an important role in Heating, Ventilation and Air Conditioning (HVAC) system. One cost effective strategy is the development of analytic fault detection and isolation (FDI) module by online monitoring the key variables of HAVC systems. This paper investigates realtime FDI for HAVC system by using online Support Vector Machine (SVM), by which we are able to train a FDI system with manageable complexity under real time working conditions. It is also proposed a new approach which allows us to detect unknown faults and updating the classifier by using these previously unknown faults. Based on the proposed approach, a semi unsupervised fault detection methodology has been developed for HVAC systems	en_US
dc.publisher	IACSIT Press	en_US
dc.relation.ispartof	International Journal of Machine Learning and Computing	en_US
dc.relation.isbasedon	10.7763/IJMLC.2011.V1.10	en_US
dc.title	Online support vector machine application for model based fault detection and isolation of HVAC system	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
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/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	1	en_US

Abstract:

Abstract—Preventive maintenance plays an important role in Heating, Ventilation and Air Conditioning (HVAC) system. One cost effective strategy is the development of analytic fault detection and isolation (FDI) module by online monitoring the key variables of HAVC systems. This paper investigates realtime FDI for HAVC system by using online Support Vector Machine (SVM), by which we are able to train a FDI system with manageable complexity under real time working conditions. It is also proposed a new approach which allows us to detect unknown faults and updating the classifier by using these previously unknown faults. Based on the proposed approach, a semi unsupervised fault detection methodology has been developed for HVAC systems

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http://hdl.handle.net/10453/33168