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Please use this identifier to cite or link to this item: http://dspace.lib.cranfield.ac.uk/handle/1826/750

Document Type: Article
Title: Gas-turbine diagnostics using artificial neural-networks for a high bypass ratio military turbofan engine
Authors: Joly, R. B.
Ogaji, S. O. T.
Singh, R.
Probert, S. D.
Issue Date: 2004
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.
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.
URI: http://dx.doi.org/10.1016/j.apenergy.2003.10.002
http://dspace.lib.cranfield.ac.uk/handle/1826/750
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