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

基于GMM聚类方法构建经验模型的机载实时模型改进方法

School of Power and Energy, Northwestern Polytechnical University, Xi'an 710072, China

Received: 29 June 2019

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.

摘要

采用建立经验模型的方法补偿机载实时模型输出与发动机测量参数输出之间的偏差，提高机载自适应模型在全飞行包线内的参数跟踪和估计性能。由于在线获取数据量较大，采用基于高斯混合模型的聚类方法实现数据压缩，用于离线训练并更新经验模型。将建立的经验模型应用在民用大涵道比涡扇发动机机载自适应模型中，仿真结果表明：基于高斯混合模型建立的经验模型能够减小机载实时模型输出误差，带经验模型的机载自适应模型的健康参数估计精度以及发动机部件故障隔离性能得到提高。