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Citation |
Hayton P, Utete S, King D, King S, Anuzis P, Tarassenko L. Philos. Transact. A Math. Phys. Eng. Sci. 2007; 365(1851): 493-514. |
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Affiliation |
Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK. |
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Copyright |
(Copyright © 2007, Royal Society Publishing) |
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DOI |
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PMID |
17255049 |
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Abstract |
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. Language: en |