IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i8p598-d74966.html
   My bibliography  Save this article

Gas-Path Health Estimation for an Aircraft Engine Based on a Sliding Mode Observer

Author

Listed:
  • Xiaodong Chang

    (College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, 29 Yudao Road, Nanjing 210016, China)

  • Jinquan Huang

    (College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, 29 Yudao Road, Nanjing 210016, China)

  • Feng Lu

    (College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, 29 Yudao Road, Nanjing 210016, China)

  • Haobo Sun

    (College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, 29 Yudao Road, Nanjing 210016, China)

Abstract

Aircraft engine gas-path health monitoring (GPHM) plays a critical role in engine health management (EHM). Among model-based approaches, the Kalman filter (KF) has been widely employed in GPHM. The main shortcoming of KF-based scheme lies in the lack of robustness against uncertainties. To enhance robustness, this paper describes a new GPHM architecture using a sliding mode observer (SMO). The convergence of the error system in uncertainty-existing circumstances is demonstrated, and the proposed method is developed to estimate components’ performance degradations regardless of modeling uncertainties. Simulations using a nonlinear model of a turbofan engine are presented, in which health monitoring problems are handled by the KF and the SMO, respectively. Results indicate the proposed approach possesses better diagnostic performance compared to the KF-based scheme, and the SMO shows its strong robustness and great potential to be applied to GPHM.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:9:y:2016:i:8:p:598-:d:74966
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/8/598/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/9/8/598/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Qingcai Yang & Shuying Li & Yunpeng Cao & Fengshou Gu & Ann Smith, 2018. "A Gas Path Fault Contribution Matrix for Marine Gas Turbine Diagnosis Based on a Multiple Model Fault Detection and Isolation Approach," Energies, MDPI, vol. 11(12), pages 1-21, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Feng Lu & Jipeng Jiang & Jinquan Huang & Xiaojie Qiu, 2018. "An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis," Energies, MDPI, vol. 11(7), pages 1-21, July.
    3. Long, Zhenhua & Bai, Mingliang & Ren, Minghao & Liu, Jinfu & Yu, Daren, 2023. "Fault detection and isolation of aeroengine combustion chamber based on unscented Kalman filter method fusing artificial neural network," Energy, Elsevier, vol. 272(C).
    4. 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.
    5. Jakovljević, Ivan & Mijailović, Radomir & Mirosavljević, Petar, 2018. "Carbon dioxide emission during the life cycle of turbofan aircraft," Energy, Elsevier, vol. 148(C), pages 866-875.
    6. 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.
    7. Feng Lu & Chunyu Jiang & Jinquan Huang & Yafan Wang & Chengxin You, 2016. "A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis," Energies, MDPI, vol. 9(10), pages 1-22, October.
    8. 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.
    9. Qingcai Yang & Shuying Li & Yunpeng Cao & Fengshou Gu & Ann Smith, 2018. "A Gas Path Fault Contribution Matrix for Marine Gas Turbine Diagnosis Based on a Multiple Model Fault Detection and Isolation Approach," Energies, MDPI, vol. 11(12), pages 1-21, November.
    10. Xiaojie Qiu & Xiaodong Chang & Jie Chen & Baiqing Fan, 2022. "Research on the Analytical Redundancy Method for the Control System of Variable Cycle Engine," Sustainability, MDPI, vol. 14(10), pages 1-11, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:9:y:2016:i:8:p:598-:d:74966. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.