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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | en |
| Published: |
2012
|
| Subjects: | |
| Online Access: | http://eprints.usm.my/44831/1/MANJEEVAN%20SINGH%20SEERA.pdf http://eprints.usm.my/44831/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1834503172826071040 |
|---|---|
| author | Seera, Manjeevan Singh |
| author_facet | Seera, Manjeevan Singh |
| author_sort | Seera, Manjeevan Singh |
| building | Hamzah Sendut Library |
| collection | Institutional Repository |
| content_provider | Universiti Sains Malaysia |
| content_source | USM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.usm.eprints.44831 |
| institution | Universiti Sains Malaysia |
| language | en |
| publishDate | 2012 |
| record_format | eprints |
| spelling | my.usm.eprints.44831 http://eprints.usm.my/44831/ Fault Detection And Diagnosis Of Induction Motors Using The Fuzzy Min-Max Neural Network And The Classification And Regression Tree 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 NonPeerReviewed application/pdf en http://eprints.usm.my/44831/1/MANJEEVAN%20SINGH%20SEERA.pdf Seera, Manjeevan Singh (2012) Fault Detection And Diagnosis Of Induction Motors Using The Fuzzy Min-Max Neural Network And The Classification And Regression Tree. PhD thesis, Universiti Sains Malaysia. |
| 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 |
| title | 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_short | 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 |
| topic | TK1-9971 Electrical engineering. Electronics. Nuclear engineering |
| url | http://eprints.usm.my/44831/1/MANJEEVAN%20SINGH%20SEERA.pdf http://eprints.usm.my/44831/ |
| url_provider | http://eprints.usm.my/ |