Improved Model for On-Board Real-Time by Constructing Empirical Model via GMM Clustering Method

Abstract

The method of constructing an empirical model is used to compensate the deviation between the output of the on-board real-time model and the engine measurement parameters, and improve the parameter tracking and estimation performance of the on-board adaptive model in the full flight envelope. Due to the large amount of data acquired online, the clustering method based on Gaussian mixture model is implemented to realize data compression for offline training and updating the empirical model. The present empirical model is applied to the on-board adaptive model of civil large bypass ratio turbofan engine. The simulation results show that the empirical model based on Gaussian mixture model can reduce the output error of on-board real-time model, and the accuracy of the health parameter estimation and engine component fault isolation performance of the on-board real-time adaptive model with empirical model are improved.