Health Parameters Estimation of Turbofan Engine Based on Improved UKF Method

In view of the shortcomings of the standard Unscented Kalman Filter (UKF), such as slow convergence speed, low precision and easy divergence, the state estimation problem is transformed into an unconstrained optimization problem, and an iterative Unscented Kalman Filter based on the optimization method is adopted. The Levenberg-Marquart nonlinear optimization method is used to improve the measurement update equation of the filtering process. Moreover, the numerical simulation of health parameters estimation on a certain turbofan engine is carried out. Simulation results show that the established parameters estimation model can accurately estimate the changes of health parameters in different performance degradation modes, and the convergence speed and stability are obviously better than the standard UKF method. In addition, the improved algorithm was successfully deployed on the NVIDIA Jetson Xavier embedded platform to complete the online estimation, which indicates that the proposed method has good real-time performance.