The importance of selecting the optimal number of principal components for fault detection using principal component analysis

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dc.contributor.advisorBraae, Martinen_ZA
dc.contributor.authorKhwambala, Patricia Helenen_ZA
dc.date.accessioned2015-01-10T13:21:29Z
dc.date.available2015-01-10T13:21:29Z
dc.date.issued2012en_ZA
dc.descriptionIncludes summary.en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractFault detection and isolation are the two fundamental building blocks of process monitoring. Accurate and efficient process monitoring increases plant availability and utilization. Principal component analysis is one of the statistical techniques that are used for fault detection. Determination of the number of PCs to be retained plays a big role in detecting a fault using the PCA technique. In this dissertation focus has been drawn on the methods of determining the number of PCs to be retained for accurate and effective fault detection in a laboratory thermal system. SNR method of determining number of PCs, which is a relatively recent method, has been compared to two commonly used methods for the same, the CPV and the scree test methods.en_ZA
dc.identifier.apacitationKhwambala, P. H. (2012). <i>The importance of selecting the optimal number of principal components for fault detection using principal component analysis</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/11930en_ZA
dc.identifier.chicagocitationKhwambala, Patricia Helen. <i>"The importance of selecting the optimal number of principal components for fault detection using principal component analysis."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2012. http://hdl.handle.net/11427/11930en_ZA
dc.identifier.citationKhwambala, P. 2012. The importance of selecting the optimal number of principal components for fault detection using principal component analysis. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Khwambala, Patricia Helen AB - Fault detection and isolation are the two fundamental building blocks of process monitoring. Accurate and efficient process monitoring increases plant availability and utilization. Principal component analysis is one of the statistical techniques that are used for fault detection. Determination of the number of PCs to be retained plays a big role in detecting a fault using the PCA technique. In this dissertation focus has been drawn on the methods of determining the number of PCs to be retained for accurate and effective fault detection in a laboratory thermal system. SNR method of determining number of PCs, which is a relatively recent method, has been compared to two commonly used methods for the same, the CPV and the scree test methods. DA - 2012 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2012 T1 - The importance of selecting the optimal number of principal components for fault detection using principal component analysis TI - The importance of selecting the optimal number of principal components for fault detection using principal component analysis UR - http://hdl.handle.net/11427/11930 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/11930
dc.identifier.vancouvercitationKhwambala PH. The importance of selecting the optimal number of principal components for fault detection using principal component analysis. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2012 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/11930en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherElectrical Engineeringen_ZA
dc.titleThe importance of selecting the optimal number of principal components for fault detection using principal component analysisen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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