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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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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science, Biomedical Research Centre
Role:
Author
Volume:
3973
Pages:
828-835
Publication date:
2006-01-01
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
URN:
uuid:9d569d29-003f-48b4-9643-b57e17b51951
Source identifiers:
61671
Local pid:
pubs:61671
ISBN:
3-540-34482-9

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