Application of the method of insignificant divergences to diagnose the technical aircraft gas turbine engine state under the transient-state conditions of its operation
https://doi.org/10.26467/2079-0619-2023-26-5-81-95
Abstract
The article deals with issues related to the use of parametric information of the transient-state gas turbine engines (GTE) operation conditions for diagnosing their technical condition during the operation. A review of general approaches to computational algorithms for the recognition and classification of the condition applicable to aircraft GTE has been carried out. The significance of analytical models in modern algorithms for assessing the technical GTE condition is emphasized. The construction of a linearized mathematical model for the transient-state condition of the generalized-scheme aircraft GTE operation has been considered. It represents a system of equations analytically combining the relative parameter divergences measured during the engine operation with the relative divergences of unmeasured thermogasdynamic parameters and geometric gas-air flow duct parameters allowing for the technical condition of gas-air channel elements to be classified. A method for constructing mathematical and diagnostic engine models, using the transient response data, has been formulated. The capability of employing a method of insignificant divergences, used to build linear (linearized) mathematical and diagnostic GTE models for the steady-state conditions of its operation, has been demonstrated as well. It is shown that, despite the structural similarity of linear models of the steady and transient-state processes, diagnostics by means of the stated above processes is based on completely different principles – under the steady-state condition, the classification of a technical condition is determined by the variation in the value of the group of controlled responses, and under the transient-state condition, this operation is based on correlating the change in the transient-state behavior. To ensure the versatility of employing proposed methods regarding various GTE designs installed on modern civil aircraft, a generalized-design aircraft GTE model – a three-shaft bypass turbojet engine with mixing flows in a common jet nozzle, has been considered.
About the Authors
O. F. MashoshinRussian Federation
Oleg F. Mashoshin, Doctor of Technical Sciences, Professor, the Head of the Aircraft Engines Chair
Moscow
I. G. Kharmats
Russian Federation
Ilya G. Kharmats, Candidate of Technical Sciences, Associate Professor, Associate Professor of the Technical Mechanics and Engineering Graphics Chair
Moscow
References
1. Zaidan, M.A., Mills, A.R., Harrison, R.F., Fleming, P.J. (2016). Gas turbine engines prognostics using bayesian hierarchical models: A variational approach. Mechanical Systems and Signal Processing, vol. 70–71, pp. 120–140. DOI: 10.1016/j.ymssp.2015.09.014
2. Zaidan, M.A., Relan, R., Mills, A.R., Harrison, R.F. (2015). Prognostics of gas turbine engine: An integrated approach. Expert Systems with Applications, vol. 42, issue 22, pp. 8472–8483. DOI: 10.1016/j.eswa.2015.07.003
3. Marins, M.A., Ribeiro, F.M.L., Netto, S.L., Da Silva, E.A.B. (2018). Improved similarity-based modeling for the classification of rotating-machine failures. Journal of the Franklin Institute, vol. 355, issue 4, pp. 1913–1930. DOI: 10.1016/j.jfranklin.2017.07.038
4. Vaezipour, A., Mosavi, A., Seiger‐ roth, U. (2013). Machine learning integrated optimization for decision-making. In: 26th European Conference on Operational Research, Rome. Available at: https://www.semanticscholar.org/paper/Machine-learning-integrated-optimization-for-making-Vaezipour-Mosavi/c1ad5937eecf961a3be64d18889d5ffeb888de50 (accessed: 27.12.2022).
5. Sina Tayarani-Bathaie, S., Khorasani, K. (2015). Fault detection and isolation of gas turbine engines using a bank of neural networks. Journal of Process Control, vol. 36, pp. 22–41. DOI: 10.1016/j.jprocont.2015.08.007
6. Amozegar, M., Khorasani, K. (2016). An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines. Neural Networks, vol. 76, pp. 106–121. DOI: 10.1016/j.neunet.2016.01.003
7. Zhernakov, S.V. (2006). Application of neural network technology for the diagnostics of the technical condition of aircraft engines. Intellektualnyye sistemy v proizvodstve, no. 2 (8), pp. 70–83. (in Russian)
8. Lu, F., Jiang, J., Huang, J., Qiu, X. (2017). Dual reduced kernel extreme learning machine for aero-engine fault diagnosis. Aerospace Science and Technology, vol. 71, pp. 742–750. DOI: 10.1016/j.ast.2017.10.024
9. Mashoshin, O.F. (2015). Evaluation of diagnostic information in solving the task of aircraft operation. Nauchnyy Vestnik MGTU GA, no. 219 (9), pp. 53–56. (in Russian)
10. Kotlyar, I.V., Gitelman, A.I. Yermolchik, V.N. et al. (1973). Transient processes in gas turbine power plants, in Kotlyar I.V. (Ed.). Leningrad: Mashinostroyeniye, 256 p. (in Russian)
11. Kazandzhan, P.K., Alekseyev, L.P., Govorov, A.N., Konovalov, N.Ye., Nechayev, Yu.N., Pavlenko, V.F., Fedorov, R.M. (1955). Theory of jet engines. Moscow: Voyenizdat, 296 p. (in Russian)
12. Ivanova, E.V., Tsymbler, M.L. (2020). Overview of modern time series management systems. Bulletin of the South Ural State University. Series: Computational Mathematics and Software Engineering, vol. 9, no. 4, pp. 79–97. DOI: 10.14529/cmse200406 (in Russian)
13. Cherkez, A.Ya. (1975). Engineering calculations of gas turbine engines using the small deviation method. 3rd ed., pererab. i dop. Moscow: Mashinostroyeniye, 380 p. (in Russian)
14. Akhmedzyanov, D.A. (2006). Nonstable regimes of aviation GTE. Vestnik Ufimskogo Gosudarstvennogo Aviatsionnogo Tekhnicheskogo Universiteta, vol. 7, no. 1, pp. 36–46. (in Russian)
15. Dmitriyev, S.A. (1996). Diagnosing the flow part of a gas turbine engine in steady and transient modes of its operation: Abstract of Technical Sc. Dissertation. Kyiv: KIIGA, 28 p. (in Russian)
16. Tsoutsanis, E., Meskin, N. (2017). Derivative-driven window-based regression method for gas turbine performance prognostics. Energy, vol. 128, pp. 302–311. DOI: 10.1016/j.energy.2017.04.006
17. Urban, L.A. (1975). Parameter selection for multiple fault diagnostics of gas turbine engines. Journal of Engineering for Gas Turbines and Power, vol. 97, no. 2, pp. 225–230. DOI: 10.1115/1.3445969
18. Kharmats, I.G. (2004). On the issue of technical condition diagnosing of aircraft gas turbine engines using linear mathematical models. Nauchnyy Vestnik MGTU GA, no. 75, pp. 87–92. (in Russian)
19. Grin, V.T. et al. (Eds.). (1982). Mathematical modeling of non-stationary processes in power plants with a gas turbine engine and other aerodynamic devices: proceedings. Moscow: TsIAM, 128 p. (in Russian)
20. Mikhnenkov, L.V., Kharmats, I.G. (2017). Parametric diagnosis of jet engines during operation using mathematical modeling: Monography. Moscow: Izdatelskiy Dom Academii Zhukovskogo, 112 p. (in Russian)
21. Runacres, T., Hong, G. (1995). Improving gas turbine engine condition assessment using a thermodinamic model. In: 2nd Pacific International Conference Aerospace Science and Technology: 6th Australian Aeronautic Conference, Melbourne, 20–23 March, pp. 41–46.
22. Akhmedzyanov, A.M., Dubravskiy, N.G., Tunakov, A.P. (1983). Diagnosis of the jet engines conditions by thermos gas dynamic parameters. Moscow: Mashinostroyeniye, 206 p. (in Russian)
23. Mashoshin, O.F. (2005). Prediction of the vibration state of aircraft engines from the standpoint of classification problems. Nauchnyy Vestnik MGTU GA, no. 85, pp. 39–45. (in Russian)
24.
Review
For citations:
Mashoshin O.F., Kharmats I.G. Application of the method of insignificant divergences to diagnose the technical aircraft gas turbine engine state under the transient-state conditions of its operation. Civil Aviation High Technologies. 2023;26(5):81-95. https://doi.org/10.26467/2079-0619-2023-26-5-81-95