Application of Neural Networks to Areoengine Adaptive Modeling

In this paper a new method based on neural networks is proposed for aeroengine adaptive real time modeling.It is thought that output data of aeroengine will bias their nominal values in any cases of non-nominal work.So the biases of output data can be used to represent the non-nominal work conditions of the aeroengine and to constitute the augment state variables.Kalman filter is designed to estimate these biases.Then the unmeasured outputs of the onboard model of the areoengine can be modified by the estimation of the unmeasured biases which are obtained through the mapping relation constructed between the measured output biases and unmeasured ones and trained by the BP neural networks offline.After modification,outputs of onboard model are the same as those of the real aeroengine,and real time onboard model has the abilities of adaptation.