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dc.coverage.spatialStudies on Induction Motor Roller Bearing Fault Analysis Using Soft Computing Techniques
dc.description.abstractBearing components in Induction Motor IM play a critical role in operational performance and reliability of the system Therefore it necessitates the development of condition monitoring and fault diagnosis system to reduce the malfunctioning of the roller bearing Vibration analysis is commonly used in the detection of roller bearing failures The fault diagnosis method comprises of pattern recognition and classification paradigms in which feature extraction is the crucial role The effective and accurate classification of roller bearing faults depends on the salient feature extraction and reducing the dimensionality In roller bearing fault diagnosis a large amount of data is collected from the operating machinery Extraction of feature is difficult as the relevant information might be submerged inside the large data pool Principal component analysis multidimensional scaling and linear discriminate analysis are used for reduction of redundant data But these feature extraction methods work effectively only in linear data with Gaussian distribution whereas vibration signal of Induction Motor is nonlinear in nature The roller bearing faults can be predicted both in time domain and in frequency domain response of the system. newline
dc.titleStudies on Induction Motor Roller Bearing Fault Analysis Using Soft Computing Techniques
dc.creator.researcherAgnes Prema Mary K
dc.subject.keywordEngineering and Technology
dc.subject.keywordEngineering Electrical and Electronic
dc.subject.keywordInduction Motor
dc.subject.keywordVibration Analysis
dc.subject.keywordPattern recognition systems
dc.contributor.guideSubburaj P
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Electrical Engineering
Appears in Departments:Faculty of Electrical Engineering

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01_title.pdfAttached File89.27 kBAdobe PDFView/Open
02_certificates.pdf330.17 kBAdobe PDFView/Open
03_abstracts.pdf85.89 kBAdobe PDFView/Open
04_acknowledgements.pdf83.08 kBAdobe PDFView/Open
05_contents.pdf91.98 kBAdobe PDFView/Open
06_list_of_tables.pdf82.15 kBAdobe PDFView/Open
07_list_of_figures.pdf86.12 kBAdobe PDFView/Open
08_list_of_abbreviations.pdf209.36 kBAdobe PDFView/Open
09_chapter1.pdf211.37 kBAdobe PDFView/Open
10_chapter2.pdf566.52 kBAdobe PDFView/Open
11_chapter3.pdf567.83 kBAdobe PDFView/Open
12_chapter4.pdf352.76 kBAdobe PDFView/Open
13_chapter5.pdf252.87 kBAdobe PDFView/Open
14_chapter6.pdf929.7 kBAdobe PDFView/Open
15_conclusion.pdf104.15 kBAdobe PDFView/Open
16_references.pdf216.21 kBAdobe PDFView/Open
17_list_of_publications.pdf111.38 kBAdobe PDFView/Open
80_recommendation.pdf77.95 kBAdobe PDFView/Open

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