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 equipme...

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Bibliographic Details
Published in:Proceedings of the Institution of Mechanical Engineers. Part G, Journal of aerospace engineering Journal of aerospace engineering, 2009-08, Vol.223 (5), p.533-541
Main Authors: King, S, Bannister, P R, Clifton, D A, Tarassenko, L
Format: Article
Language:English
Subjects:
Aerospace engineering
Aerospace engines
Airplane engines
Condition monitoring
Diagnostic software
Diagnostic systems
Diagnostics
Downtime
Efficiency
Engineers
Integrity
Mathematical models
Monitoring systems
Normality
OEM
Probabilistic methods
Probability theory
Running
Sensitivity
Suppliers
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Summary: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.
ISSN:0954-4100
2041-3025
DOI:10.1243/09544100JAERO414