FAULT DETECTION AND PROGNOSTICS OF INSULATED GATE BIPOLAR TRANSISTOR (IGBT) USING A K-NEAREST NEIGHBOR CLASSIFICATION ALGORITHM

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dc.contributor.advisorPecht, Michaelen_US
dc.contributor.authorSutrisno, Edwinen_US
dc.contributor.departmentMechanical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2013-10-10T05:37:09Z
dc.date.available2013-10-10T05:37:09Z
dc.date.issued2013en_US
dc.description.abstractInsulated Gate Bipolar Transistor (IGBT) is a power semiconductor device commonly used in medium to high power applications from household appliances, automotive, and renewable energy. Health assessment of IGBT under field use is of interest due to costly system downtime that may be associated with IGBT failures. Conventional reliability approaches were shown by experimental data to suffer from large uncertainties when predicting IGBT lifetimes, partly due to their inability to adapt to varying loading conditions and part-to-part differences. This study developed a data-driven prognostic method to individually assess IGBT health based on operating data obtained from run-to-failure experiments. IGBT health was classified into healthy and faulty using a K-Nearest Neighbor Centroid Distance classification algorithm. A feature weight optimization method was developed to determine the influence of each feature toward classifying IGBT's health states.en_US
dc.identifier.urihttp://hdl.handle.net/1903/14687
dc.subject.pqcontrolledEngineeringen_US
dc.subject.pqcontrolledMathematicsen_US
dc.subject.pquncontrolledClassificationen_US
dc.subject.pquncontrolledIGBTen_US
dc.subject.pquncontrolledPHMen_US
dc.subject.pquncontrolledPrognosticsen_US
dc.titleFAULT DETECTION AND PROGNOSTICS OF INSULATED GATE BIPOLAR TRANSISTOR (IGBT) USING A K-NEAREST NEIGHBOR CLASSIFICATION ALGORITHMen_US
dc.typeThesisen_US

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