Gas-turbine diagnostics using artificial neural-networks for a high bypass ratio military turbofan engine

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dc.contributor.author Joly, R. B. -
dc.contributor.author Ogaji, S. O. T. -
dc.contributor.author Singh, R. -
dc.contributor.author Probert, S. D. -
dc.date.accessioned 2011-10-31T23:02:47Z
dc.date.available 2011-10-31T23:02:47Z
dc.date.issued 2004-08-01T00:00:00Z -
dc.identifier.citation R. B. Joly, S. O. T. Ogaji, R. Singh and S. D. Probert, Gas-turbine diagnostics using artificial neural-networks for a high bypass ratio military turbofan engine, Applied Energy, Volume 78, Issue 4, August 2004, Pages 397-418. -
dc.identifier.issn 0306-2619 -
dc.identifier.uri http://dx.doi.org/10.1016/j.apenergy.2003.10.002 -
dc.identifier.uri http://dspace.lib.cranfield.ac.uk/handle/1826/750
dc.description.abstract The Tristar aircraft, operated by the Royal Air Force, fly many thousands of hours per year in the transport and air-to-air refuelling roles. A large amount of engine data is recorded for each of the Rolls-Royce RB211-524B4 engines: it is used to aid the maintenance process. Data are also generated during test-bed engine ground-runs after repair and overhaul. In order to use recorded engine data more effectively, this paper assesses the feasibility of a pro-active engine diagnostic-tool using artificial neural networks (ANNs). Engine-health monitoring is described and the theory behind an ANN is described. An engine diagnostic structure is proposed using several ANNs. The top level distinguishes between single-component faults (SCFs) and double-component faults (DCFs). The middle-level class includes components, or component pairs, which are faulty. The bottom level estimates the values of the engine-independent parameters, for each engine component, based on a set of engine data using dependent parameters. The DCF results presented in this paper illustrate the potential for ANNs as diagnostic tools. However, there are also a number of features of ANN applications that are user-defined: ANN designs; the number of training epochs used; the training function employed; the method of performance assessment; and the degree of deterioration for each engine-component's performance parameter. en_UK
dc.language.iso en_UK -
dc.publisher Elsevier Science B.V., Amsterdam. en_UK
dc.subject Jet engines en_UK
dc.subject Aircraft propulsion en_UK
dc.title Gas-turbine diagnostics using artificial neural-networks for a high bypass ratio military turbofan engine en_UK
dc.type Article -


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