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dc.contributor.authorNguyen, Viet Hungen
dc.identifier.citationNguyen, V. H. (2018). Model-based diagnosis and prognosis of induction motors under stator winding fault. Doctoral thesis, Nanyang Technological University, Singapore.en
dc.description.abstractInduction machines are widely used in industries and essential parts of industrial systems. Despite their rugged construction, they are subject to fault due to aging, severe operating conditions, and harsh environments. Industrial surveys have shown that stator winding accounts for a significant portion of faults in electrical machines. Stator winding inter-turn short is one of the most common root causes of stator winding fault which can spread over and lead to catastrophic damages. In this thesis, a framework for diagnosis and prognosis of electrical machines under stator winding inter-turn short fault, and the associated techniques for sub-problems including early fault detection, fault severity estimation, and degradation modeling and RUL estimation, are proposed. Model-based is the applied technique including parity equation approach using sequence component model, multiple-model approach, and particle-filter based approach.en
dc.format.extent231 p.en
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleModel-based diagnosis and prognosis of induction motors under stator winding faulten
dc.contributor.supervisorWang Dan Weien
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.description.degreeDoctor of Philosophyen
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