Complex Condition Monitoring System of Aircraft Gas Turbine Engine

Researches show that probability-statistical methods application, especially at the early stage of the aviation Gas Turbine Engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods is considered. According to the purpose of this problem training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. For GTE technical condition more adequate model making dynamics of skewness and kurtosis coefficients- changes are analysed. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE workand output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stage-by-stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine technical condition was made.

Authors:

• A. M. Pashayev
• D. D. Askerov
• C. Ardil
• R. A. Sadiqov
• P. S. Abdullayev

Keywords:

• aviation gas turbine engine
• technical condition
• fuzzy logic
• neural networks
• fuzzy statistics

References:

[1] Pashayev A.M., Sadiqov R.A., Abdullayev P.S. "Fuzzy-Neural Approach for Aircraft Gas Turbine Engines Diagnosing", AIAA
1st Intelligent Systems Technical Conference (Chicago, Illinois,
Sep. 20-22, 2004), AIAA-2004-6222, 2004
[2] Sadiqov R.A., Makarov N.V., Abdullayev P.S., "Problems of aviation GTE technical condition estimation in operation", Proceedings of V International Symposium an Aeronautical Sciences «New Aviation Technologies of the XXI century», Sec. 4, "Perspective technologies of flight tests and problems of
flight safety increase", MAKS-99, Zhukovsky, Russia, August,1999.
[3] Pashayev A.M., Sadiqov R.A., Abdullayev P.S. Complex identification technique of aircraft gas turbine engine's health,
ASME TurboEXPO 2003, Atlanta GA, USA, Paper GT2003-38704
[4] Ivanov L.A. and etc. The technique of civil aircraft GTE technical condition diagnosing and forecasting on registered rotor vibrations
parameters changes in service.- M: GosNII GA, 1984.-88p.
[5] Doroshko S.M. The control and diagnosing of GTE technical condition on vibration parameters. - M.: Transport, 1984.-128 p.
[6] Abasov M.T., Sadiqov A.H., Aliyarov R.Y. Fuzzy neural networks in the system of oil and gas geology and geophysics // Third
International Conference on Application of Fuzzy Systems and Soft computing/ Wiesbaden, Germany, 1998,- p.108-117.
[7] Yager R.R., Zadeh L.A. (Eds). Fuzzy sets, neural networks and soft computing. VAN Nostrand Reinhold. N.-Y. - 4,1994.
[8] Mohamad H. Hassoun. Fundamentals of artificial neutral networks
/ A Bradford Book. The MIT press Cambridge, Massachusetts,
London, England, 1995
[9] Pashayev A.M., Sadiqov R.A., Makarov N.V., Abdullayev P.S.
Estimation of GTE technical condition on flight information
//Abstracts of XI All-Russian interinstit.science-techn.conf. "Gaz
turbine and combined installations and engines" dedicated to 170
year MGTU nam. N.E.BAUMAN, sec. 1., N.E.BAUMAN
MGTU., 15-17 november., Moscow.-2000.- p.22-24
[10] Pashayev A.M., Sadiqov R.A., Abdullayev P.S. Neuro-Fuzzy
Identification of Aircraft Gas Turbine Engine's Technical
Condition. Proceedings of the International Conference on
Computational Intelligence (ICCI 2004), May 27-29, 2004, Nicosia, North Cyprus, 2004, p.136-142
[11] Pashayev F.M., Askerov D.D., Sadiqov R.A., Abdullayev P.S.
Identification technique of aircraft gas turbine engine's health. 7th Biennial Conference on Engineering Systems Design and Analysis, July 19-22, 2004, Manchester, United Kingdom, Paper
ESDA2004-58168