Knowledge and data jointly driven aeroengine gas path performance assessment method  

作　　者：Zhe WANG Xuyun FU Rui ZHANG Zhengfeng BAI Xiangzhao XIA Wei JIANG 

机构地区：[1]Department of Mechanical Engineering,Harbin Institute of Technology,Weihai 264209,China [2]Weihai Key Laboratory of Intelligent Operation and Maintenance,Harbin Institute of Technology,Weihai 264209,China [3]Control System Design Laboratory,AECC ShenYang Engine Research Institute,Shenyang 110015,China

出　　处：《Chinese Journal of Aeronautics》2024年第5期533-557,共25页中国航空学报（英文版）

基　　金：This study was co-supported by the National Key Research and Development Program of China(No.2020YFB1709800);the National Science and Technology Major Project(No.J2019-I-0001-0001).

摘　　要：Aeroengines,as the sole power source for aircraft,play a vital role in ensuring flight safety.The gas path,which represents the fundamental pathway for airflow within an aeroengine,directly impacts the aeroengine's performance,fuel efficiency,and safety.Therefore,timely and accurate evaluation of gas path performance is of paramount importance.This paper proposes a knowledge and data jointly driven aeroengine gas path performance assessment method,combining Fingerprint and gas path parameter deviation values.Firstly,Fingerprint is used to correct gas path parameter deviation values,eliminating parameter shifts caused by non-component performance degradation.Secondly,coarse errors are removed using the Romanovsky criterion for short-term data divided by an equal-length overlapping sliding window.Thirdly,an Ensemble Empirical Mode Decomposition and Non-Local Means(EEMD-NLM)filtering method is designed to“clean”data noise,completing the preprocessing for gas path parameter deviation values.Afterward,based on the characteristics of gas path parameter deviation values,a Dynamic Temporary Blended Network(DTBN)model is built to extract its temporal features,cascaded with Multi-Layer Perceptron(MLP),and combined with Fingerprint to construct a Dynamic Temporary Blended AutoEncoder(DTB-AutoEncoder).Eventually,by training this improved autoencoder,the aeroengine gas path multi-component performance assessment model is formed,which can sufficiently decouple the nonlinear mapping relationship between aeroengine gas path multi-component performance degradation and gas path parameter deviation values,thereby achieving the performance assessment of engine gas path components.Through practical application cases,the effectiveness of this model in assessing the aeroengine gas path multi-component performance is verified.

关 键 词：Performance assessment AEROENGINE FINGERPRINT Gas path parameter deviation values Jointly drive 

分 类 号：V231.9[航空宇航科学与技术—航空宇航推进理论与工程]