A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals
This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS)....
| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
Curtin University
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/458 |
| _version_ | 1848743384158568448 |
|---|---|
| author | Latuny, Jonny |
| author_facet | Latuny, Jonny |
| author_sort | Latuny, Jonny |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS). The test results showed that the ball fault (BF) classifier successfully achieved 100% accuracy without mis-classification, while the outer race fault (ORF), inner race fault (IRF) and no fault (NF) classifiers achieved mixed results. |
| first_indexed | 2025-11-14T05:44:43Z |
| format | Thesis |
| id | curtin-20.500.11937-458 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T05:44:43Z |
| publishDate | 2013 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-4582017-02-20T06:40:51Z A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals Latuny, Jonny This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS). The test results showed that the ball fault (BF) classifier successfully achieved 100% accuracy without mis-classification, while the outer race fault (ORF), inner race fault (IRF) and no fault (NF) classifiers achieved mixed results. 2013 Thesis http://hdl.handle.net/20.500.11937/458 en Curtin University fulltext |
| spellingShingle | Latuny, Jonny A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals |
| title | A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals |
| title_full | A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals |
| title_fullStr | A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals |
| title_full_unstemmed | A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals |
| title_short | A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals |
| title_sort | sensitivity comparison of neuro-fuzzy feature extraction methods from bearing failure signals |
| url | http://hdl.handle.net/20.500.11937/458 |