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Novelty detection in large-vehicle turbocharger operation

Abstract:

We develop novelty detection techniques for the analysis of data from a large-vehicle engine turbocharger in order to illustrate how abnormal events of operational significance may be identified with respect to a model of normality. Results are validated using polynomial function modelling and reduced dimensionality visualisation techniques to show that system operation can be automatically classified into one of three distinct state spaces, each corresponding to a unique set of running condi...

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Publication status:
Published

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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
Host title:
New Trends in Applied Artificial Intelligence, Proceedings
Volume:
4570
Pages:
591-600
Publication date:
2007-01-01
EISSN:
1611-3349
ISSN:
0302-9743
ISBN:
9783540733225
Keywords:
Pubs id:
pubs:61659
UUID:
uuid:1aebc47e-f183-4ff1-ae73-27607223ab92
Local pid:
pubs:61659
Source identifiers:
61659
Deposit date:
2013-11-17

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