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 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.
Hayton
Paul
P
Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK.
Utete
Simukai
S
King
Dennis
D
King
Steve
S
Anuzis
Paul
P
Tarassenko
Lionel
L
D000401:Aircraft / Q000295:instrumentation*; D000465:Algorithms; D003198:Computer Simulation; D003252:Construction Materials / Q000032:analysis*; D004738:Engineering / Q000295:instrumentation / Q000379:methods; D004867:Equipment Design; D019544:Equipment Failure Analysis / Q000295:instrumentation / Q000379:methods*; D008283:Maintenance / Q000379:methods; D008422:Materials Testing / Q000379:methods*; D008962:Models, Theoretical; D012815:Signal Processing, Computer-Assisted; D014159:Transducers; D014732:Vibration