典型文献
A step parameters prediction model based on transfer process neural network for exhaust gas temperature estimation after washing aero-engines
文献摘要：
The prediction of Exhaust Gas Temperature Margin (EGTM) after washing aero-engines can provide a theoretical basis for airlines not only to evaluate the energy-saving effect and emission reduction,but also to formulate reasonable maintenance plans.However,the EGTM encounters step changes after washing aeroengines,while,in the traditional models,a persistence tendency exists between the prediction results and the previous data,resulting in low accuracy in prediction.In order to solve the problem,this paper develops a step parameters prediction model based on Transfer Process Neural Networks (TPNN).Especially,"step parameters" represent the parameters that can reflect EGTM step changes.They are analyzed in this study,and thus the model concentrates on the prediction of step changes rather than the extension of data trends.Transfer learning is used to handle the problem that few cleaning records result in few step changes for model learning.In comparison with Long Short-Term Memory (LSTM) and Kernel Extreme Learning Machine (KELM) models,the effectiveness of the proposed method is verified on CFM56-5B engine data.
文献关键词：
中图分类号：
作者姓名：
Zhiqi YAN;Shisheng ZHONG;Lin LIN;Zhiquan CUI;Minghang ZHAO
作者机构：
School of Mechatronics Engineering,Harbin Institute of Technology,Harbin 150001,China;Department of Vehicle Engineering,Harbin Institute of Technology,Weihai 264209,China;School of Naval Architecture and Ocean Engineering,Harbin Institute of Technology,Weihai 264209,China
文献出处：
引用格式：
[1]Zhiqi YAN;Shisheng ZHONG;Lin LIN;Zhiquan CUI;Minghang ZHAO-.A step parameters prediction model based on transfer process neural network for exhaust gas temperature estimation after washing aero-engines)[J].中国航空学报（英文版）,2022(03):98-111
A类：
airlines,TPNN
B类：
step,parameters,prediction,transfer,process,neural,network,exhaust,gas,temperature,estimation,after,washing,Exhaust,Gas,Temperature,Margin,EGTM,can,provide,theoretical,basis,not,only,evaluate,energy,saving,emission,reduction,but,also,formulate,reasonable,maintenance,plans,However,encounters,changes,aeroengines,while,traditional,models,persistence,tendency,exists,between,results,previous,data,resulting,low,accuracy,In,order,solve,problem,this,paper,develops,Transfer,Process,Neural,Networks,Especially,represent,that,reflect,They,are,analyzed,study,thus,concentrates,rather,than,extension,trends,learning,used,handle,few,cleaning,records,comparison,Long,Short,Term,Memory,Kernel,Extreme,Learning,Machine,KELM,effectiveness,proposed,method,verified,CFM56,5B
AB值：
0.586661
相似文献
机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
