Automatic feature selection using multiobjective cluster optimization for fault detection in a heating ventilation and air conditioning system

Yuwono, M; Su, SW; Guo, Y; Li, J; West, S; Wall, J

Automatic feature selection using multiobjective cluster optimization for fault detection in a heating ventilation and air conditioning system

Yuwono, M Su, SW

Guo, Y Li, J West, S Wall, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013, 2014, pp. 171 - 176
Issue Date:: 2014-11-14

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Field	Value	Language
dc.contributor.author	Yuwono, M	en_US
dc.contributor.author	Su, SW https://orcid.org/0000-0002-5720-8852	en_US
dc.contributor.author	Guo, Y	en_US
dc.contributor.author	Li, J	en_US
dc.contributor.author	West, S	en_US
dc.contributor.author	Wall, J	en_US
dc.date.issued	2014-11-14	en_US
dc.identifier.citation	Proceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013, 2014, pp. 171 - 176	en_US
dc.identifier.isbn	9781479932511	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120758
dc.description.abstract	© 2013 IEEE. The performance of Automatic Fault Detection and Diagnostics (AFDD) algorithms to identify faults in complex building Heating Ventilation and Air-Conditioning (HVAC) systems depend on the appropriateness of features. This paper proposes a knowledge-discovery approach for discovering characteristic features using Multi-Objective Clustering Rapid Centroid Estimation (MOC-RCE). The proposed method has been tested on experimental fault data from the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) research project 1312-RP Winter 2008 dataset. An experiment involving 100 clustering trials shows that using the proposed method, on average 15 characteristic features have been selected from the original 320 features. Sensitivity, specificity, accuracy, precision, and F-score values of greater than 95% are achieved with the provided features.	en_US
dc.relation.ispartof	Proceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013	en_US
dc.relation.isbasedon	10.1109/AIMS.2013.34	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Automatic feature selection using multiobjective cluster optimization for fault detection in a heating ventilation and air conditioning system	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	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	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2013 IEEE. The performance of Automatic Fault Detection and Diagnostics (AFDD) algorithms to identify faults in complex building Heating Ventilation and Air-Conditioning (HVAC) systems depend on the appropriateness of features. This paper proposes a knowledge-discovery approach for discovering characteristic features using Multi-Objective Clustering Rapid Centroid Estimation (MOC-RCE). The proposed method has been tested on experimental fault data from the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) research project 1312-RP Winter 2008 dataset. An experiment involving 100 clustering trials shows that using the proposed method, on average 15 characteristic features have been selected from the original 320 features. Sensitivity, specificity, accuracy, precision, and F-score values of greater than 95% are achieved with the provided features.

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http://hdl.handle.net/10453/120758