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dc.contributor.advisorDevaney, Michael J.eng
dc.contributor.authorTeotrakool, Kaptan, 1974-eng
dc.date.issued2007eng
dc.date.submitted2007 Falleng
dc.descriptionThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.eng
dc.descriptionTitle from title screen of research.pdf file (viewed on March 3, 2008)eng
dc.descriptionIncludes bibliographical references.eng
dc.descriptionVita.eng
dc.descriptionThesis (Ph. D.) University of Missouri-Columbia 2007.eng
dc.description.abstractThis dissertation presents a novel method to detect bearing defects in Adjustable Speed Drives (ASD's), which are increasingly used in many commercial and industrial applications. The harmonics in pulse-width-modulation (PWM) input voltage waveforms and EMI noise in ASD systems complicate the detection of bearing-fault-induced frequency components in the current signals. The proposed method accomplishes bearing fault detection in ASD's by combining Motor Current Signature Analysis (MCSA), Wavelet Packet Decomposition (WPD), a Genetic Algorithm (GA), and a Support Vector Machine (SVM). The SVM in conjunction with the GA is applied to the rms values of the wavelet packet coefficients to obtain significant wavelet packet nodes which produce optimal classifiers for classifying both healthy and defective bearings in ASD syste.eng
dc.description.bibrefIncludes bibliographical referenceseng
dc.identifier.merlinb62408355eng
dc.identifier.oclc212627974eng
dc.identifier.urihttps://doi.org/10.32469/10355/4845eng
dc.identifier.urihttps://hdl.handle.net/10355/4845
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsOpenAccess.eng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
dc.subject.lcshVariable speed driveseng
dc.subject.lcshPulse-duration modulationeng
dc.subject.lcshElectromagnetic interferenceeng
dc.titleAdjustable speed drive bearing fault detection via support vector machine incorporating feature selection using genetic algorithmeng
dc.typeThesiseng
thesis.degree.disciplineElectrical engineering (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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