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|Title:||Model-based diagnosis and prognosis of induction motors under stator winding fault||Authors:||Nguyen, Viet Hung||Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2018||Source:||Nguyen, V. H. (2018). Model-based diagnosis and prognosis of induction motors under stator winding fault. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||Induction 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.||URI:||https://hdl.handle.net/10356/88954
|DOI:||10.32657/10220/46022||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Theses|
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