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Probabilistic approach to the condition monitoring of aerospace engines

Abstract:
The provision of TotalCare® styled service offerings by original equipment manufacture (OEM) suppliers of high-integrity assets is intended to provide improved levels of system availability to the operator. A key element of such service offerings is the ability to minimize unplanned equipment downtime, and the utilization of advanced diagnostic and prognostic monitoring tools is a significant component in achieving this. Monitoring methods, founded on novelty detection technologies, are now a well-established condition monitoring technique. This approach is particularly appropriate for monitoring high-integrity plant where fault conditions arise with extremely low levels of probability. The approach described in this article is to establish empirically based models of normality that are guided by engineering knowledge and utilize key features normally used by expert engineers. However, rather than consider generic modelling approaches, it is proposed that application of models that adapt their sensitivity to the operation of individual assets offer greater prognostic efficiency. This article demonstrates how this can be achieved by considering asset-specific models that adapt the threshold of alerting in accordance with the observed normal running of the plant.
Publication status:
Published

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Publisher copy:
10.1243/09544100JAERO414

Authors

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


Journal:
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING More from this journal
Volume:
223
Issue:
G5
Pages:
533-541
Publication date:
2009-08-01
DOI:
EISSN:
2041-3025
ISSN:
0954-4100


Language:
English
Keywords:
Pubs id:
pubs:61648
UUID:
uuid:07d9f190-3b9f-425e-a914-ed7d31d52888
Local pid:
pubs:61648
Source identifiers:
61648
Deposit date:
2013-11-17
ARK identifier:

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