Combined support vector novelty detection for multi-channel combustion data
Multi-channel combustion data, consisting of gas pressure and two combustion chamber luminosity measurements, are investigated in the prediction of combustion instability. Wavelet analysis is used for feature extraction. A SVM approach is applied for novelty detection and the construction of a model...
| Main Authors: | , , , |
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| Format: | Journal article |
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IEEE
2007
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| _version_ | 1797055982154547200 |
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| author | Clifton, L Yin, H Clifton, D Zhang, Y |
| author_facet | Clifton, L Yin, H Clifton, D Zhang, Y |
| author_sort | Clifton, L |
| collection | OXFORD |
| description | Multi-channel combustion data, consisting of gas pressure and two combustion chamber luminosity measurements, are investigated in the prediction of combustion instability. Wavelet analysis is used for feature extraction. A SVM approach is applied for novelty detection and the construction of a model of normal system operation. Novelty scores generated by classifiers from different channels are combined to give a final decision of data novelty. We compare four novelty score combination mechanisms, and illustrate their complementary relationship in assessing data novelty. |
| first_indexed | 2024-03-06T19:17:03Z |
| format | Journal article |
| id | oxford-uuid:18c4922f-f29e-4d25-9fef-a6d586148bfc |
| institution | University of Oxford |
| last_indexed | 2024-03-06T19:17:03Z |
| publishDate | 2007 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:18c4922f-f29e-4d25-9fef-a6d586148bfc2022-03-26T10:45:17ZCombined support vector novelty detection for multi-channel combustion dataJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:18c4922f-f29e-4d25-9fef-a6d586148bfcSymplectic Elements at OxfordIEEE2007Clifton, LYin, HClifton, DZhang, YMulti-channel combustion data, consisting of gas pressure and two combustion chamber luminosity measurements, are investigated in the prediction of combustion instability. Wavelet analysis is used for feature extraction. A SVM approach is applied for novelty detection and the construction of a model of normal system operation. Novelty scores generated by classifiers from different channels are combined to give a final decision of data novelty. We compare four novelty score combination mechanisms, and illustrate their complementary relationship in assessing data novelty. |
| spellingShingle | Clifton, L Yin, H Clifton, D Zhang, Y Combined support vector novelty detection for multi-channel combustion data |
| title | Combined support vector novelty detection for multi-channel combustion data |
| title_full | Combined support vector novelty detection for multi-channel combustion data |
| title_fullStr | Combined support vector novelty detection for multi-channel combustion data |
| title_full_unstemmed | Combined support vector novelty detection for multi-channel combustion data |
| title_short | Combined support vector novelty detection for multi-channel combustion data |
| title_sort | combined support vector novelty detection for multi channel combustion data |
| work_keys_str_mv | AT cliftonl combinedsupportvectornoveltydetectionformultichannelcombustiondata AT yinh combinedsupportvectornoveltydetectionformultichannelcombustiondata AT cliftond combinedsupportvectornoveltydetectionformultichannelcombustiondata AT zhangy combinedsupportvectornoveltydetectionformultichannelcombustiondata |