Parametric diagnostics of the condition of a dual-flow turbojet engine using neural network simulation of the operating process

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.