Model-based diagnosis and prognosis of induction motors under stator winding fault
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 por...
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sg-ntu-dr.10356-889542023-07-04T16:27:28Z Model-based diagnosis and prognosis of induction motors under stator winding fault Nguyen, Viet Hung Wang Dan Wei School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering 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. Doctor of Philosophy 2018-09-19T01:34:19Z 2019-12-06T17:14:31Z 2018-09-19T01:34:19Z 2019-12-06T17:14:31Z 2018 Thesis Nguyen, V. H. (2018). Model-based diagnosis and prognosis of induction motors under stator winding fault. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/88954 http://hdl.handle.net/10220/46022 10.32657/10220/46022 en 231 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering Nguyen, Viet Hung Model-based diagnosis and prognosis of induction motors under stator winding fault |
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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. |
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Wang Dan Wei |
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Wang Dan Wei Nguyen, Viet Hung |
format |
Theses and Dissertations |
author |
Nguyen, Viet Hung |
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Nguyen, Viet Hung |
title |
Model-based diagnosis and prognosis of induction motors under stator winding fault |
title_short |
Model-based diagnosis and prognosis of induction motors under stator winding fault |
title_full |
Model-based diagnosis and prognosis of induction motors under stator winding fault |
title_fullStr |
Model-based diagnosis and prognosis of induction motors under stator winding fault |
title_full_unstemmed |
Model-based diagnosis and prognosis of induction motors under stator winding fault |
title_sort |
model-based diagnosis and prognosis of induction motors under stator winding fault |
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2018 |
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https://hdl.handle.net/10356/88954 http://hdl.handle.net/10220/46022 |
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1772827668439367680 |