TY  - JOUR
PY  - 2007//
TI  - Static and dynamic novelty detection methods for jet engine health monitoring
JO  - Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
A1  - Hayton, Paul
A1  - Utete, Simukai
A1  - King, D.
A1  - King, Sara
A1  - Anuzis, Paul
A1  - Tarassenko, Lionel
SP  - 493
EP  - 514
VL  - 365
IS  - 1851
N2  - 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.<p /> <p>Language: en</p> 
LA  - en
SN  - 1364-503X
UR  - http://dx.doi.org/10.1098/rsta.2006.1954
ID  - ref1
ER  -