文档介绍：Gas-turbine Diagnostics Using Artificial works for a
High Bypass Ratio Military Turbofan Engine
R B Joly, S O T Ogaji*, R Singh, and . Probert
School of Engineering, Cranfield University, Bedfordshire MK43 OAL, UK
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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 ponent faults (SCFs) and double-
component faults (DCFs). The middle-level class ponents, ponent
pairs, which are faulty. The bottom level estimates the values of the engine-
independent parameters, for each ponent, 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 ponent’s performance parameter.
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Abbreviations and Nomenclature
ANN Artificial work
BPR Bypass ratio
DCF ponent Fault
EDMS Exhaust-debris monitoring system
EFH Engine Flying Hour
EGT Exhaust-Gas’ Temperature
* Cor