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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.

Original publication

DOI

10.1098/rsta.2006.1954

Type

Journal article

Journal

Philos Trans A Math Phys Eng Sci

Publication Date

15/02/2007

Volume

365

Pages

493 - 514

Keywords

Aircraft, Algorithms, Computer Simulation, Construction Materials, Engineering, Equipment Design, Equipment Failure Analysis, Maintenance, Materials Testing, Models, Theoretical, Signal Processing, Computer-Assisted, Transducers, Vibration