Abstract
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.
Highlights
It is known that a considerable part of dual-flow turbojet engine (DFTE) faults and failures includes parametric failures which consist in a discrepancy between the values of the monitored parameters of the DFTE and the requirements of the specifications
Nowadays the artificial neural network (ANN) technology is one of the fastest growing fields related to artificial intelligence
In the study [1], through the example of a single-shaft aircraft engine and a two-shaft engine, it was shown that diagnostics of the condition based on ANN modeling of the operating process has an advantage over other methods [1,5,6,7,8,9]
Summary
It is known that a considerable part of DFTE faults and failures includes parametric failures which consist in a discrepancy between the values of the monitored parameters of the DFTE and the requirements of the specifications. Parametric methods of diagnostics of a condition are used for monitoring and prevention of similar failures. These methods are based on custom processing and analysis of the values of the thermogasdynamic parameters and other parameters monitored on a running DFTE [1,2,3,4,5,6,7,8,9,10,12]. Nowadays the artificial neural network (ANN) technology is one of the fastest growing fields related to artificial intelligence It is successfully applied in various fields of science and technology for image recognition, diagnostics of the condition of complex technical facilities, etc. It is important to carry out research to improve the efficiency of the ANN method for parametric diagnostics of the DFTE condition
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