Establishment of Aero-Engine Improved On-Board Adaptive Model with Contracted Kalman Filter Estimation

Due to the limited number of sensors in the aeroengine, the estimation of performance degradation of the engine is inaccurate. In this paper, an improved on-board adaptive model with contracted Kalman filter is proposed. This is accomplished by constructing a transformation matrix to reduce the dimension of the health parameter vector, and taking the estimated deviation of the simplified Kalman filter as the goal of minimization, and using genetic algorithm and deep learning models to build more accurate health parameters to reflect the engine performance. Use the inverse transform to obtain the original health parameters. The simulation results show that the improved on-board adaptive model established in this paper can still estimate the health parameters with high accuracy when the measured parameter dimension is less than the health parameter dimension, and the accuracy of improved on-board adaptive model is very high.