Static and dynamic novelty detection methods for jet engine health monitoring.
Novelty detection requires models of normality to be learnt from training data known to be normal. The first model considered in this paper is a static model trained to detect novel events associated with changes in the vibration spectra recorded from a jet engine. We describe how the distribution o...
| Những tác giả chính: | , , , , , |
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| Định dạng: | Journal article |
| Ngôn ngữ: | English |
| Được phát hành: |
2007
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| _version_ | 1826268512683819008 |
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| author | Hayton, P Utete, S King, D King, S Anuzis, P Tarassenko, L |
| author_facet | Hayton, P Utete, S King, D King, S Anuzis, P Tarassenko, L |
| author_sort | Hayton, P |
| collection | OXFORD |
| description | Novelty detection requires models of normality to be learnt from training data known to be normal. The first model considered in this paper is a static model trained to detect novel events associated with changes in the vibration spectra recorded from a jet engine. We describe how the distribution of energy across the harmonics of a rotating shaft can be learnt by a support vector machine model of normality. The second model is a dynamic model partially learnt from data using an expectation-maximization-based method. This model uses a Kalman filter to fuse performance data in order to characterize normal engine behaviour. Deviations from normal operation are detected using the normalized innovations squared from the Kalman filter. |
| first_indexed | 2024-03-06T21:10:49Z |
| format | Journal article |
| id | oxford-uuid:3e17d68d-7c26-4a35-ad9c-11d7e0760df1 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T21:10:49Z |
| publishDate | 2007 |
| record_format | dspace |
| spelling | oxford-uuid:3e17d68d-7c26-4a35-ad9c-11d7e0760df12022-03-26T14:23:26ZStatic and dynamic novelty detection methods for jet engine health monitoring.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:3e17d68d-7c26-4a35-ad9c-11d7e0760df1EnglishSymplectic Elements at Oxford2007Hayton, PUtete, SKing, DKing, SAnuzis, PTarassenko, LNovelty detection requires models of normality to be learnt from training data known to be normal. The first model considered in this paper is a static model trained to detect novel events associated with changes in the vibration spectra recorded from a jet engine. We describe how the distribution of energy across the harmonics of a rotating shaft can be learnt by a support vector machine model of normality. The second model is a dynamic model partially learnt from data using an expectation-maximization-based method. This model uses a Kalman filter to fuse performance data in order to characterize normal engine behaviour. Deviations from normal operation are detected using the normalized innovations squared from the Kalman filter. |
| spellingShingle | Hayton, P Utete, S King, D King, S Anuzis, P Tarassenko, L Static and dynamic novelty detection methods for jet engine health monitoring. |
| title | Static and dynamic novelty detection methods for jet engine health monitoring. |
| title_full | Static and dynamic novelty detection methods for jet engine health monitoring. |
| title_fullStr | Static and dynamic novelty detection methods for jet engine health monitoring. |
| title_full_unstemmed | Static and dynamic novelty detection methods for jet engine health monitoring. |
| title_short | Static and dynamic novelty detection methods for jet engine health monitoring. |
| title_sort | static and dynamic novelty detection methods for jet engine health monitoring |
| work_keys_str_mv | AT haytonp staticanddynamicnoveltydetectionmethodsforjetenginehealthmonitoring AT utetes staticanddynamicnoveltydetectionmethodsforjetenginehealthmonitoring AT kingd staticanddynamicnoveltydetectionmethodsforjetenginehealthmonitoring AT kings staticanddynamicnoveltydetectionmethodsforjetenginehealthmonitoring AT anuzisp staticanddynamicnoveltydetectionmethodsforjetenginehealthmonitoring AT tarassenkol staticanddynamicnoveltydetectionmethodsforjetenginehealthmonitoring |