Gas Turbine Engine Identification Based on a Bank of Self-Tuning Wiener Models Using Fast Kernel Extreme Learning Machine

Gas Turbine Engine Identification Based on a Bank of Self-Tuning Wiener Models Using Fast Kernel Extreme Learning Machine

In order to simultaneously obtain global optimal model structure and coefficients, this paper proposes a novel Wiener model to identify the dynamic and static behavior of a gas turbine engine. An improved kernel extreme learning machine is presented to build up a bank of self-tuning block-oriented W...

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Bibliographic Details
Main Authors: Feng Lu, Yu Ye, Jinquan Huang
Format: Article
Language:English
Published: MDPI AG 2017-09-01
Series:Energies
Subjects:
gas turbine engine
system identification
block-oriented model
kernel extreme learning machine
reduced-matrix inversion
Online Access:https://www.mdpi.com/1996-1073/10/9/1363
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spelling doaj-b8f53b3ee275414cb0302a62777930d92020-11-25T00:53:00ZengMDPI AGEnergies1996-10732017-09-01109136310.3390/en10091363en10091363Gas Turbine Engine Identification Based on a Bank of Self-Tuning Wiener Models Using Fast Kernel Extreme Learning MachineFeng Lu0Yu Ye1Jinquan Huang2Jiangsu Province Key Laboratory of Aerospace Power Systems, College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, ChinaJiangsu Province Key Laboratory of Aerospace Power Systems, College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, ChinaJiangsu Province Key Laboratory of Aerospace Power Systems, College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, ChinaIn order to simultaneously obtain global optimal model structure and coefficients, this paper proposes a novel Wiener model to identify the dynamic and static behavior of a gas turbine engine. An improved kernel extreme learning machine is presented to build up a bank of self-tuning block-oriented Wiener models; the time constant values of linear dynamic element in Wiener model are designed to tune engine operating conditions. Reduced-dimension matrix inversion incorporated with the fast leave one out cross validation strategy is utilized to decrease computational time for the selection of engine model feature parameters. An optimization algorithm is no longer needed compared to the former method. The contribution of this study is that a more convenient and appropriate methodology is developed to describe aircraft engine thermodynamic behavior during its static and dynamic operations. The methodology is evaluated in terms of computational efforts, dynamic and static estimation accuracy through a case study involving data that are generated by general aircraft engine simulation. The results confirm our viewpoints in this paper.https://www.mdpi.com/1996-1073/10/9/1363gas turbine enginesystem identificationblock-oriented modelkernel extreme learning machinereduced-matrix inversion
collection DOAJ
language English
format Article
sources DOAJ
author Feng Lu
Yu Ye
Jinquan Huang
spellingShingle Feng Lu
Yu Ye
Jinquan Huang
Gas Turbine Engine Identification Based on a Bank of Self-Tuning Wiener Models Using Fast Kernel Extreme Learning Machine
Energies
gas turbine engine
system identification
block-oriented model
kernel extreme learning machine
reduced-matrix inversion
author_facet Feng Lu
Yu Ye
Jinquan Huang
author_sort Feng Lu
title Gas Turbine Engine Identification Based on a Bank of Self-Tuning Wiener Models Using Fast Kernel Extreme Learning Machine
title_short Gas Turbine Engine Identification Based on a Bank of Self-Tuning Wiener Models Using Fast Kernel Extreme Learning Machine
title_full Gas Turbine Engine Identification Based on a Bank of Self-Tuning Wiener Models Using Fast Kernel Extreme Learning Machine
title_fullStr Gas Turbine Engine Identification Based on a Bank of Self-Tuning Wiener Models Using Fast Kernel Extreme Learning Machine
title_full_unstemmed Gas Turbine Engine Identification Based on a Bank of Self-Tuning Wiener Models Using Fast Kernel Extreme Learning Machine
title_sort gas turbine engine identification based on a bank of self-tuning wiener models using fast kernel extreme learning machine
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2017-09-01
description In order to simultaneously obtain global optimal model structure and coefficients, this paper proposes a novel Wiener model to identify the dynamic and static behavior of a gas turbine engine. An improved kernel extreme learning machine is presented to build up a bank of self-tuning block-oriented Wiener models; the time constant values of linear dynamic element in Wiener model are designed to tune engine operating conditions. Reduced-dimension matrix inversion incorporated with the fast leave one out cross validation strategy is utilized to decrease computational time for the selection of engine model feature parameters. An optimization algorithm is no longer needed compared to the former method. The contribution of this study is that a more convenient and appropriate methodology is developed to describe aircraft engine thermodynamic behavior during its static and dynamic operations. The methodology is evaluated in terms of computational efforts, dynamic and static estimation accuracy through a case study involving data that are generated by general aircraft engine simulation. The results confirm our viewpoints in this paper.
topic gas turbine engine
system identification
block-oriented model
kernel extreme learning machine
reduced-matrix inversion
url https://www.mdpi.com/1996-1073/10/9/1363
work_keys_str_mv AT fenglu gasturbineengineidentificationbasedonabankofselftuningwienermodelsusingfastkernelextremelearningmachine
AT yuye gasturbineengineidentificationbasedonabankofselftuningwienermodelsusingfastkernelextremelearningmachine
AT jinquanhuang gasturbineengineidentificationbasedonabankofselftuningwienermodelsusingfastkernelextremelearningmachine
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