{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/7460"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/7460","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"The application of advanced signal processing techniques to the condition monitoring of electrical machine drive systems","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Barendse, Paul Stanley"],"institution":"Department of Electrical Engineering","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Pillay, Pragasen"],"committee_chairs":[],"committee_members":[],"year":2007,"date_issued":"2007","date_published":"2007","updated_at":"2026-07-22T22:23:31Z","subjects":[],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/7460","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Pillay, Pragasen"]},{"key":"dc:creator","label":"Author","values":["Barendse, Paul Stanley"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-09-15T07:24:44Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-09-15T07:24:44Z"]},{"key":"dc:date.issued","label":"Date","values":["2007"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Electrical Engineering"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cape Town"]},{"key":"dc:type","label":"Dc Type","values":["Doctoral Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["PhD"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/7460"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Includes bibliographical references (leaves 128-129)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["The application of advanced signal processing techniques to the condition monitoring of electrical machine drive systems"]}]}],"canonical_facts":{"dc:contributor.advisor":["Pillay, Pragasen"],"dc:creator":["Barendse, Paul Stanley"],"dc:date.accessioned":["2014-09-15T07:24:44Z"],"dc:date.available":["2014-09-15T07:24:44Z"],"dc:date.issued":["2007"],"dc:description":["Includes bibliographical references (leaves 128-129)."],"dc:description.abstract":["The thesis examines the use of two time-frequency domain signal processing tools in its application to condition monitoring of electrical machine drive systems. 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