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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.2006.1931

Type

Journal article

Journal

Philosophical transactions. series a, mathematical, physical, and engineering sciences

Publication Date

02/2007

Volume

365

Pages

493 - 514

Addresses

Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK.

Keywords

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