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