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|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.
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.|
|Appears in Collections:||Staff publications - School of Engineering|
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