Aircraft gas turbine engine health monitoring system by real flight data

Abstract

Modern condition monitoring-based methods are used to reduce maintenance costs, increase aircraft safety, and reduce fuel consumption. In the literature, parameters such as engine fan speeds, vibration, oil pressure, oil temperature, exhaust gas temperature (EGT), and fuel flow are used to determine performance deterioration in gas turbine engines. In this study, a new model was developed to get information about the gas turbine engine's condition. For this model, multiple regression analysis was carried out to determine the effect of the flight parameters on the EGT parameter and the artificial neural network (ANN) method was used in the identification of EGT parameter. At the end of the study, a network that predicts the EGT parameter with the smallest margin of error has been developed. An interface for instant monitoring of the status of the aircraft engine has been designed in MATLAB Simulink. Any performance degradation that may occur in the aircraft's gas turbine engine can be easily detected graphically or by the engine performance deterioration value. Also, it has been indicated that it could be a new indicator that informs the pilots in the event of a fault in the sensor of the EGT parameter that they monitor while flying.