MATEC Web Conf.
Volume 224, 2018International Conference on Modern Trends in Manufacturing Technologies and Equipment (ICMTMTE 2018)
|Number of page(s)||8|
|Section||General problems of mechanical engineering: design, optimization, maintenance|
|Published online||30 October 2018|
Parametric diagnostics of the condition of a dual-flow turbojet engine using neural network simulation of the operating process
Ufa State Aviation Technical University, Ufa city, the Russian Federation
2 Department of Aviation Engines, Ufa city, the Russian Federation
* Corresponding author: firstname.lastname@example.org
The article presents a method of parametric diagnostics of the condition of a dual-flow turbojet engine (DFTE). The method is based on the identification (determination) of the condition of the DFTE components (the compressor, combustion chamber, turbine) with application of a mathematical model of the operating process which is presented as an artificial neural network (ANN) model. This model describes the relation between the monitored parameters of the DFTE (the air temperatures (Tlpc*, Thpc*) beyond the low pressure compressor (LPC) and the high pressure compressor (HPC), the pressure beyond the LPC (Plpc), the fuel consumption rate (Gf), the gas temperatures (Thpt*, Tlpt*) beyond the high pressure turbine (HPT) and the low pressure turbine (LPT)) and the parameters of the condition of its components (the efficiencies of the LPC and the HPC (ηlpc*, ηhpc*), the stagnation pressure recovery factor in the combustion chamber (σcc), the efficiencies of the HPT and the LPT (ηhpt*, ηlpt*)). The parameters of the condition of the engine components (ηlpc*, ηhpc*, σcc, ηhpt*, ηlpt*) are the similarity criteria (integral criteria) which enable to identify the condition of the DFTE components to a high degree of reliability. Such analysis enables to detect defects at an early stage, even if the values of the monitored parameters (Тlpc*, Тhpc*, Plpc, Gf, Тhpt*, Тlpt*) are within the permissible limits. We provide the sequence for development of the ANN model and the results of its performance study during the parametric diagnostics of the condition of the DFTE.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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