Affiliation of Author(s):能源与动力学院
Journal:Tuijin Jishu
Abstract:Aiming at the fact that Kalman filter is apt to misjudge the gas path health parameters of a commercial aircraft engine since available on-board sensors are unevenly distributed and the number of them is usually less than the number of health parameters, a Neural Network corrected Kalman Filter (NN-KF) algorithm is proposed. Individuals of the algorithm are corrected by Back Propagation-Neural Network (BP-NN) and their weights are calculated on the basis of Particle Filter (PF) during each sampling period in order to estimate mean and covariance, then KF is adopted to update individuals. The mean vector is regarded as estimated result every time step. Several types of abrupt gas path faults of a commercial aircraft engine at multiple fly points have been numerically simulated and all 10 health parameters are estimated based on 7 measured outputs. The simulation results suggest that the hybrid method compared with BP-NN and Unscented Kalman Filter (UKF) reduces estimated errors by averages of 34.6% and 47.9%, respectively. © 2018, Editorial Department of Journal of Propulsion Technology. All right reserved.
ISSN No.:1001-4055
Translation or Not:no
Date of Publication:2018-11-01
Co-author:Gu, Jia-Hui,Feng Lu
Correspondence Author:Huang Jinquan
Date of Publication:2018-11-01