Gas turbine engine condition monitoring using statistical and neural network methods

Author(s):  
V.C. Patel
Author(s):  
Seonghee Kho ◽  
Jayoung Ki ◽  
Miyoung Park ◽  
Changduk Kong ◽  
Kyungjae Lee

This study is aim to be programmed the simulation which is available for real-time performance analysis so that is to be developed gas turbine engine’s condition monitoring system with analyzing difference between performance analysis results and measuring data from test cell. In addition, test cell created by this study have been developed to use following applications: to use for learning principals and mechanism of gas turbine engine in school, and to use performance test and its further research for variable operating conditions in associated institutes. The maximum thrust of the micro turbojet engine is 137 N (14 kgf) at 126,000 rpm of rotor rotational speed if the Jet A1 kerosene fuel is used. The air flow rate is measured by the inflow air speed of duct, and the fuel flow is measured by a volumetric fuel flowmeter. Temperatures and pressures are measured at the atmosphere, the compressor inlet and outlet and the turbine outlet. The thrust stand was designed and manufactured to measure accurately the thrust by the load cell. All measuring sensors are connected to a DAQ (Data Acquisition) device, and the logging data are used as function parameters of the program, LabVIEW. The LabVIEW is used to develop the engine condition monitoring program. The proposed program can perform both the reference engine model performance analysis at an input condition and the real-time performance analysis with real-time variables. By comparing two analysis results the engine condition can be monitored. Both engine performance analysis data and monitoring results are displayed by the GUI (Graphic User Interface) platform.


1998 ◽  
Vol 31 (4) ◽  
pp. 161-165
Author(s):  
G.G. Kulikov ◽  
T.V. Breikin ◽  
V.Y. Arkov ◽  
P.J. Fleming

1997 ◽  
Vol 30 (18) ◽  
pp. 67-71 ◽  
Author(s):  
Timofei Breikin ◽  
Valentin Arkov ◽  
Gennady Kulikov ◽  
Visakan Kadirkamanathan ◽  
Vijay Patel

Aviation ◽  
2013 ◽  
Vol 17 (2) ◽  
pp. 52-56 ◽  
Author(s):  
Mykola Kulyk ◽  
Sergiy Dmitriev ◽  
Oleksandr Yakushenko ◽  
Oleksandr Popov

A method of obtaining test and training data sets has been developed. These sets are intended for training a static neural network to recognise individual and double defects in the air-gas path units of a gas-turbine engine. These data are obtained by using operational process parameters of the air-gas path of a bypass turbofan engine. The method allows sets that can project some changes in the technical conditions of a gas-turbine engine to be received, taking into account errors that occur in the measurement of the gas-dynamic parameters of the air-gas path. The operation of the engine in a wide range of modes should also be taken into account.


2004 ◽  
Vol 37 (6) ◽  
pp. 1037-1042
Author(s):  
V.Yu. Rutkovsky ◽  
S.D. Zemlyakov ◽  
V.M. Sukhanov ◽  
V.M. Glumov

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