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Learning shape for jet engine novelty detection

Abstract:
Application of a neural network approach to data exploration and the generation of a model of system normality is described for use in novelty detection of vibration characteristics of a modern jet engine. The analysis of the shape of engine vibration signatures is shown to improve upon existing methods of engine vibration testing, in which engine vibrations are conventionally compared with a fixed vibration threshold. A refinement of the concept of "novelty scoring" in this approach is also presented. © Springer-Verlag Berlin Heidelberg 2006.
Publication status:
Published

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Publisher copy:
10.1007/11760191_121

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More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Institute of Biomedical Engineering
Role:
Author
Volume:
3973
Pages:
828-835
Host title:
ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS
Publication date:
2006-01-01
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
Source identifiers:
61671
ISBN:
3540344829
Pubs id:
pubs:61671
UUID:
uuid:9d569d29-003f-48b4-9643-b57e17b51951
Local pid:
pubs:61671
Deposit date:
2013-11-17

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