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

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  • Feng Lu

    (Jiangsu Province Key Laboratory of Aerospace Power Systems, College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China)

  • Yu Ye

    (Jiangsu Province Key Laboratory of Aerospace Power Systems, College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China)

  • Jinquan Huang

    (Jiangsu Province Key Laboratory of Aerospace Power Systems, College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China)

Abstract

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.

Suggested Citation

  • Feng Lu & Yu Ye & Jinquan Huang, 2017. "Gas Turbine Engine Identification Based on a Bank of Self-Tuning Wiener Models Using Fast Kernel Extreme Learning Machine," Energies, MDPI, vol. 10(9), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1363-:d:111296
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    References listed on IDEAS

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    1. Feng Lu & Yafan Wang & Jinquan Huang & Yihuan Huang, 2015. "Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter," Energies, MDPI, vol. 8(12), pages 1-17, December.
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    Cited by:

    1. Bo Zhang & Jianping Yuan & Jianfei Pan & Xiaoyu Wu & Jianjun Luo & Li Qiu, 2017. "Controllability and Leader-Based Feedback for Tracking the Synchronization of a Linear-Switched Reluctance Machine Network," Energies, MDPI, vol. 10(11), pages 1-18, October.

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