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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| 語言: | English |
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2012
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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 |