Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/546293
Title: Optimization of gmr sensor location on induction motor for online rotor fault prediction using wavelet transform and machine learning
Researcher: Kavitha, S
Guide(s): Bhuvaneshwari, N S
Keywords: Engineering
Engineering and Technology
Engineering Electrical and Electronic
University: Anna University
Completed Date: 2023
Abstract: Induction motor (IM) is used in various industrial applications newlinebecause of reliability and robustness. In Induction Motor (IM), broken rotor newlinebar fault leads to excess amount of current flow in the stator and causes newlineunbalanced magnetic field distribution between stator and rotor. The newlineunbalanced magnetic field damages the stator winding and increases the newlinevibrations in IM. Condition monitoring based fault detection of motor at early newlinestage is essential for smooth run of motor. IM fault detection techniques are newlineclassified as three categories such as Signal-based techniques, Model-based newlinetechniques, Knowledge-based techniques. In signal-based techniques, newlineparameters such as current, voltage and leakage flux are used for continuous newlinemonitoring of Induction motor. Motor Current Signature Analysis (MCSA) is newlinewidely used for rotor bar fault detection. Many numbers of MCSA based newlinemethods are developed for rotor bar fault detection through spectral analysis newlinetechniques such as autoregressive-based spectrum methods, Wavelet newlineTransforms, Taylor Kalman approach, low-frequency, load torque newlineoscillations and high-resolution parameter estimation. newline
Pagination: xxv,194p.
URI: http://hdl.handle.net/10603/546293
Appears in Departments:Faculty of Electrical Engineering

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01_title.pdfAttached File24.53 kBAdobe PDFView/Open
02_prelimpages.pdf3.25 MBAdobe PDFView/Open
03_content.pdf16.91 kBAdobe PDFView/Open
04_abstract.pdf88.56 kBAdobe PDFView/Open
05_chapter1.pdf33.81 kBAdobe PDFView/Open
06_chapter2.pdf238.46 kBAdobe PDFView/Open
07_chapter3.pdf197.76 kBAdobe PDFView/Open
08_chapter4.pdf2.24 MBAdobe PDFView/Open
09_chapter5.pdf1.36 MBAdobe PDFView/Open
10_chapter6.pdf749.67 kBAdobe PDFView/Open
11_annexures.pdf106.8 kBAdobe PDFView/Open
80_recommendation.pdf84.43 kBAdobe PDFView/Open
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