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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-m...

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Publisher copy:
10.1098/rsta.2006.1931

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Journal:
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Volume:
365
Issue:
1851
Pages:
493-514
Publication date:
2007-02-01
DOI:
EISSN:
1471-2962
ISSN:
1364-503X
Source identifiers:
61531
Language:
English
Keywords:
Pubs id:
pubs:61531
UUID:
uuid:3e17d68d-7c26-4a35-ad9c-11d7e0760df1
Local pid:
pubs:61531
Deposit date:
2013-11-17

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