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Health Parameter Estimation with Second-Order Sliding Mode Observer for a Turbofan Engine

Author

Listed:
  • Xiaodong Chang

    (Jiangsu Province Key Laboratory of Aerospace Power System, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Jinquan Huang

    (Jiangsu Province Key Laboratory of Aerospace Power System, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Feng Lu

    (Jiangsu Province Key Laboratory of Aerospace Power System, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Abstract

In this paper the problem of health parameter estimation in an aero-engine is investigated by using an unknown input observer-based methodology, implemented by a second-order sliding mode observer (SOSMO). Unlike the conventional state estimator-based schemes, such as Kalman filters (KF) and sliding mode observers (SMO), the proposed scheme uses a “reconstruction signal” to estimate health parameters modeled as artificial inputs, and is not only applicable to long-time health degradation, but reacts much quicker in handling abrupt fault cases. In view of the inevitable uncertainties in engine dynamics and modeling, a weighting matrix is created to minimize such effect on estimation by using the linear matrix inequalities (LMI). A big step toward uncertainty modeling is taken compared with our previous SMO-based work, in that uncertainties are considered in a more practical form. Moreover, to avoid chattering in sliding modes, the super-twisting algorithm (STA) is employed in observer design. Various simulations are carried out, based on the comparisons between the KF-based scheme, the SMO-based scheme in our earlier research, and the proposed method. The results consistently demonstrate the capabilities and advantages of the proposed approach in health parameter estimation.

Suggested Citation

  • Xiaodong Chang & Jinquan Huang & Feng Lu, 2017. "Health Parameter Estimation with Second-Order Sliding Mode Observer for a Turbofan Engine," Energies, MDPI, vol. 10(7), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:1040-:d:105386
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    References listed on IDEAS

    as
    1. Xiaodong Chang & Jinquan Huang & Feng Lu & Haobo Sun, 2016. "Gas-Path Health Estimation for an Aircraft Engine Based on a Sliding Mode Observer," Energies, MDPI, vol. 9(8), pages 1-15, July.
    2. Feng Lu & Jinquan Huang & Yiqiu Lv, 2013. "Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach," Energies, MDPI, vol. 6(1), pages 1-22, January.
    3. Joly, R. B. & Ogaji, S. O. T. & Singh, R. & Probert, S. D., 2004. "Gas-turbine diagnostics using artificial neural-networks for a high bypass ratio military turbofan engine," Applied Energy, Elsevier, vol. 78(4), pages 397-418, August.
    4. Daehyun Kim & Taedong Goh & Minjun Park & Sang Woo Kim, 2015. "Fuzzy Sliding Mode Observer with Grey Prediction for the Estimation of the State-of-Charge of a Lithium-Ion Battery," Energies, MDPI, vol. 8(11), pages 1-20, November.
    5. Ogaji, S.O.T. & Marinai, L. & Sampath, S. & Singh, R. & Prober, S.D., 2005. "Gas-turbine fault diagnostics: a fuzzy-logic approach," Applied Energy, Elsevier, vol. 82(1), pages 81-89, September.
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    Cited by:

    1. Xiaodong Chang & Jinquan Huang & Feng Lu, 2019. "Sensor Fault Tolerant Control for Aircraft Engines Using Sliding Mode Observer," Energies, MDPI, vol. 12(21), pages 1-15, October.
    2. Zhao, Hang & Liao, Zengbu & Liu, Jinxin & Li, Ming & Liu, Wei & Wang, Lei & Song, Zhiping, 2022. "A highly robust thrust estimation method with dissimilar redundancy framework for gas turbine engine," Energy, Elsevier, vol. 245(C).
    3. Zijian Qiang & Jinquan Huang & Feng Lu & Xiaodong Chang, 2019. "Robust Sensor Fault Reconstruction via a Bank of Second-Order Sliding Mode Observers for Aircraft Engines," Energies, MDPI, vol. 12(14), pages 1-20, July.

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