The application of advanced signal processing techniques to the condition monitoring of electrical machine drive systems

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dc.contributor.advisorPillay, Pragasenen_ZA
dc.contributor.authorBarendse, Paul Stanleyen_ZA
dc.date.accessioned2014-09-15T07:24:44Z
dc.date.available2014-09-15T07:24:44Z
dc.date.issued2007en_ZA
dc.descriptionIncludes bibliographical references (leaves 128-129).en_ZA
dc.description.abstractThe thesis examines the use of two time-frequency domain signal processing tools in its application to condition monitoring of electrical machine drive systems. The mathematical and signal processing tools which are explored are wavelet analysis and a non-stationary adaptive signal processing algorithm. Four specific applications are identified for the research. These applications were specifically chosen to encapsulate important issues in condition monitoring of variable speed drive systems. The main aim of the project is to highlight the need for fault detection during machine transients and to illustrate the effectiveness of incorporating and adapting these new class of algorithms to detect faults in electrical machine drive systems during non-stationary conditions.en_ZA
dc.identifier.apacitationBarendse, P. S. (2007). <i>The application of advanced signal processing techniques to the condition monitoring of electrical machine drive systems</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/7460en_ZA
dc.identifier.chicagocitationBarendse, Paul Stanley. <i>"The application of advanced signal processing techniques to the condition monitoring of electrical machine drive systems."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2007. http://hdl.handle.net/11427/7460en_ZA
dc.identifier.citationBarendse, P. 2007. The application of advanced signal processing techniques to the condition monitoring of electrical machine drive systems. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Barendse, Paul Stanley AB - The thesis examines the use of two time-frequency domain signal processing tools in its application to condition monitoring of electrical machine drive systems. The mathematical and signal processing tools which are explored are wavelet analysis and a non-stationary adaptive signal processing algorithm. Four specific applications are identified for the research. These applications were specifically chosen to encapsulate important issues in condition monitoring of variable speed drive systems. The main aim of the project is to highlight the need for fault detection during machine transients and to illustrate the effectiveness of incorporating and adapting these new class of algorithms to detect faults in electrical machine drive systems during non-stationary conditions. DA - 2007 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2007 T1 - The application of advanced signal processing techniques to the condition monitoring of electrical machine drive systems TI - The application of advanced signal processing techniques to the condition monitoring of electrical machine drive systems UR - http://hdl.handle.net/11427/7460 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/7460
dc.identifier.vancouvercitationBarendse PS. The application of advanced signal processing techniques to the condition monitoring of electrical machine drive systems. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2007 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/7460en_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 application of advanced signal processing techniques to the condition monitoring of electrical machine drive systemsen_ZA
dc.typeDoctoral Thesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnamePhDen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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