论文:2020,Vol:38,Issue(3):507-514
引用本文:
孙浩, 郭迎清, 赵万里. 基于GMM聚类方法构建经验模型的机载实时模型改进方法[J]. 西北工业大学学报
SUN Hao, GUO Yingqing, ZHAO Wanli. Improved Model for On-Board Real-Time by Constructing Empirical Model via GMM Clustering Method[J]. Northwestern polytechnical university

基于GMM聚类方法构建经验模型的机载实时模型改进方法
孙浩, 郭迎清, 赵万里
西北工业大学 动力与能源学院, 陕西 西安 710072
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。
关键词:    航空发动机    机载实时模型    高斯混合模型    经验模型   
Improved Model for On-Board Real-Time by Constructing Empirical Model via GMM Clustering Method
SUN Hao, GUO Yingqing, ZHAO Wanli
School of Power and Energy, Northwestern Polytechnical University, Xi'an 710072, China
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.
Key words:    aero-engine    onboard real-time model    gaussian mixture model    empirical model   
收稿日期: 2019-06-29     修回日期:
DOI: 10.1051/jnwpu/20203830507
通讯作者:     Email:
作者简介: 孙浩(1993-),西北工业大学博士研究生,主要从事航空发动机故障诊断与容错控制研究。
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