Adaptive noise cancelling applied to machine condition monitoring

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dc.contributor.advisorJongens, A W Den_ZA
dc.contributor.authorBremer, Paul Grahamen_ZA
dc.date.accessioned2014-10-11T12:06:11Z
dc.date.available2014-10-11T12:06:11Z
dc.date.issued1990en_ZA
dc.descriptionIncludes bibliography.en_ZA
dc.description.abstractThe objective of this thesis is to determine whether Adaptive Noise Cancelling can be used successfully in determining the state of machine elements. In addition, this thesis was used to gain experience in real-time computing. This was done by designing and building a real-time machine monitoring package using an IBM PC and a TMS 320C25 digital signal-processing chip manufactured by Texas Instruments. To determine which adaptive algorithm should be used in the package, experiments were carried out on a computer with different types of adaptive noise cancelling algorithms, the two main ones being the Least-Mean-Squares (LMS) and Recursive-Least-Squares (RLS) algorithms.en_ZA
dc.identifier.apacitationBremer, P. G. (1990). <i>Adaptive noise cancelling applied to machine condition monitoring</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/8349en_ZA
dc.identifier.chicagocitationBremer, Paul Graham. <i>"Adaptive noise cancelling applied to machine condition monitoring."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 1990. http://hdl.handle.net/11427/8349en_ZA
dc.identifier.citationBremer, P. 1990. Adaptive noise cancelling applied to machine condition monitoring. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Bremer, Paul Graham AB - The objective of this thesis is to determine whether Adaptive Noise Cancelling can be used successfully in determining the state of machine elements. In addition, this thesis was used to gain experience in real-time computing. This was done by designing and building a real-time machine monitoring package using an IBM PC and a TMS 320C25 digital signal-processing chip manufactured by Texas Instruments. To determine which adaptive algorithm should be used in the package, experiments were carried out on a computer with different types of adaptive noise cancelling algorithms, the two main ones being the Least-Mean-Squares (LMS) and Recursive-Least-Squares (RLS) algorithms. DA - 1990 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 1990 T1 - Adaptive noise cancelling applied to machine condition monitoring TI - Adaptive noise cancelling applied to machine condition monitoring UR - http://hdl.handle.net/11427/8349 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/8349
dc.identifier.vancouvercitationBremer PG. Adaptive noise cancelling applied to machine condition monitoring. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 1990 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/8349en_ZA
dc.language.isoeng
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 and Electronic Engineeringen_ZA
dc.titleAdaptive noise cancelling applied to machine condition monitoringen_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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