典型文献
Adaptive modification of turbofan engine nonlinear model based on LSTM neural networks and hybrid optimization method
文献摘要：
An accurate and reliable turbofan engine model which can describe its dynamic behavior within the full flight envelop and lifecycle plays a critical role in performance optimization,con-troller design and fault diagnosis.However,due to the performance differences caused by the tol-erance of engine manufacturing and assembly,and performance degradation during continuously stringent environmental regulations,the model accuracy is severely reduced.In this paper,an adap-tive modification method of turbofan engine nonlinear Component-Llevel Model(CLM)based on Long Short-Term Memory(LSTM)Neural Network(NN)and hybrid optimization algorithm is pro-posed.First,a dynamic compensator with a combined LSTM NN architecture is constructed to compensate for the initial error between the experimental data and CLM of a turbofan engine under health condition.Then,a sensitivity analysis approach based on the entropy coefficient and technique for order preference by similarity to an ideal solution integrated evaluation is devel-oped to choose the unmeasurable health parameters to be adjusted.Finally,a parallel hybrid opti-mization algorithm is developed to complete the adaptive model modification when the performance degrades.The proposed method is verified on a military low-bypass twin-spool turbo-fan engine,and the experimental results show the effectiveness of the proposed method.
文献关键词：
中图分类号：
作者姓名：
Yanhua MA;Xian DU;Ximing SUN
作者机构：
School of Microelectronics,Dalian University of Technology,Dalian 116024,China;Key Laboratory of Intelligent Control and Optimization for Industrial Equipment,Ministry of Education,Dalian University of Technology,Dalian 116024,China;School of Control Science and Engineering,Dalian University of Technology,Dalian 116024,China
文献出处：
引用格式：
[1]Yanhua MA;Xian DU;Ximing SUN-.Adaptive modification of turbofan engine nonlinear model based on LSTM neural networks and hybrid optimization method)[J].中国航空学报（英文版）,2022(09):314-332
A类：
turbofan,Llevel
B类：
Adaptive,modification,engine,nonlinear,model,neural,networks,hybrid,optimization,method,An,accurate,reliable,which,can,describe,its,dynamic,behavior,within,full,flight,envelop,lifecycle,plays,critical,role,performance,troller,design,fault,diagnosis,However,due,differences,caused,tol,erance,manufacturing,assembly,degradation,during,continuously,stringent,environmental,regulations,accuracy,severely,reduced,In,this,paper,Component,Model,CLM,Long,Short,Term,Memory,Neural,Network,NN,algorithm,First,compensator,combined,architecture,constructed,compensate,initial,error,between,experimental,data,under,health,condition,Then,sensitivity,analysis,approach,entropy,coefficient,technique,order,preference,similarity,ideal,solution,integrated,evaluation,choose,unmeasurable,parameters,adjusted,Finally,parallel,developed,complete,adaptive,when,degrades,proposed,verified,military,low,bypass,twin,spool,results,show,effectiveness
AB值：
0.575683
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