Estimating Deterioration Level of Aircraft Engines

Abstract

This paper describes an approach to estimate the deterioration level of aircraft engines using engine monitoring data and a physics-based engine model. The estimation process is carried out by a neural network, which is trained by data generated using a physical-based engine model complemented with an empirically derived engine deterioration model. The deterioration model allows manipulation of several engine health parameters, such as module efficiency and flow capacity, to simulate engine deterioration. Simulated sensor outputs are used to build independent transfer functions relating the sensor values to a deterioration level. A calibration model corrects the sensor readings to a reference condition so that the effect of variation of operating condition is minimized. The proposed approach can be used to assess engine deterioration level in real time. The proposed deterioration estimation approach is validated using real-world engine data.