title: Static and dynamic novelty detection methods for jet engine health monitoring.
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.
authors.affiliation: Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK.
authors.forename: Paul
authors.initials: P
authors.lastname: Hayton
authors.forename: Simukai
authors.initials: S
authors.lastname: Utete
authors.forename: Dennis
authors.initials: D
authors.lastname: King
authors.forename: Steve
authors.initials: S
authors.lastname: King
authors.forename: Paul
authors.initials: P
authors.lastname: Anuzis
authors.forename: Lionel
authors.initials: L
authors.lastname: Tarassenko
mesh_terms: 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