Fault Detection And Diagnosis Of Induction Motors Using The Fuzzy Min-Max Neural Network And The Classification And Regression Tree

In this thesis, a novel approach to detecting and diagnosing comprehensive fault conditions of Induction Motors (IMs) using an Fuzzy Min-Max (FMM) neural network and the Classification and Regression Tree (CART) is proposed. The model, known as FMM-CART, exploits the advantages of both FMM and the C...

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Main Author:	Seera, Manjeevan Singh
Format:	Thesis
Language:	English
Published:	2012
Subjects:	TK1-9971 Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.usm.my/44831/1/MANJEEVAN%20SINGH%20SEERA.pdf
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id	my-usm-ep.44831
record_format	uketd_dc
spelling	my-usm-ep.448312019-07-03T01:57:33Z Fault Detection And Diagnosis Of Induction Motors Using The Fuzzy Min-Max Neural Network And The Classification And Regression Tree 2012-05 Seera, Manjeevan Singh TK1-9971 Electrical engineering. Electronics. Nuclear engineering In this thesis, a novel approach to detecting and diagnosing comprehensive fault conditions of Induction Motors (IMs) using an Fuzzy Min-Max (FMM) neural network and the Classification and Regression Tree (CART) is proposed. The model, known as FMM-CART, exploits the advantages of both FMM and the CART for undertaking data classification and rule extraction problems. Modifications to FMM and the CART are introduced in order for the resulting model to work efficiently. In order to compare the FMM-CART performance, benchmark data sets from motor bearing faults and from the UCI machine learning repository are used for analysis, with the results discussed and compared with those from other methods. 2012-05 Thesis http://eprints.usm.my/44831/ http://eprints.usm.my/44831/1/MANJEEVAN%20SINGH%20SEERA.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Kejuteraan Elektrik & Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TK1-9971 Electrical engineering Electronics Nuclear engineering
spellingShingle	TK1-9971 Electrical engineering Electronics Nuclear engineering Seera, Manjeevan Singh Fault Detection And Diagnosis Of Induction Motors Using The Fuzzy Min-Max Neural Network And The Classification And Regression Tree
description	In this thesis, a novel approach to detecting and diagnosing comprehensive fault conditions of Induction Motors (IMs) using an Fuzzy Min-Max (FMM) neural network and the Classification and Regression Tree (CART) is proposed. The model, known as FMM-CART, exploits the advantages of both FMM and the CART for undertaking data classification and rule extraction problems. Modifications to FMM and the CART are introduced in order for the resulting model to work efficiently. In order to compare the FMM-CART performance, benchmark data sets from motor bearing faults and from the UCI machine learning repository are used for analysis, with the results discussed and compared with those from other methods.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Seera, Manjeevan Singh
author_facet	Seera, Manjeevan Singh
author_sort	Seera, Manjeevan Singh
title	Fault Detection And Diagnosis Of Induction Motors Using The Fuzzy Min-Max Neural Network And The Classification And Regression Tree
title_short	Fault Detection And Diagnosis Of Induction Motors Using The Fuzzy Min-Max Neural Network And The Classification And Regression Tree
title_full	Fault Detection And Diagnosis Of Induction Motors Using The Fuzzy Min-Max Neural Network And The Classification And Regression Tree
title_fullStr	Fault Detection And Diagnosis Of Induction Motors Using The Fuzzy Min-Max Neural Network And The Classification And Regression Tree
title_full_unstemmed	Fault Detection And Diagnosis Of Induction Motors Using The Fuzzy Min-Max Neural Network And The Classification And Regression Tree
title_sort	fault detection and diagnosis of induction motors using the fuzzy min-max neural network and the classification and regression tree
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuteraan Elektrik & Elektronik
publishDate	2012
url	http://eprints.usm.my/44831/1/MANJEEVAN%20SINGH%20SEERA.pdf
_version_	1747821407787548672

Fault Detection And Diagnosis Of Induction Motors Using The Fuzzy Min-Max Neural Network And The Classification And Regression Tree

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