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Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach

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  • Feng Lu

    (College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
    Aviation Motor Control System Institute, Aviation Industry Corporation of China, Wuxi 214063, China)

  • Jinquan Huang

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

  • Yiqiu Lv

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

Abstract

Different approaches for gas path performance estimation of dynamic systems are commonly used, the most common being the variants of the Kalman filter. The extended Kalman filter (EKF) method is a popular approach for nonlinear systems which combines the traditional Kalman filtering and linearization techniques to effectively deal with weakly nonlinear and non-Gaussian problems. Its mathematical formulation is based on the assumption that the probability density function (PDF) of the state vector can be approximated to be Gaussian. Recent investigations have focused on the particle filter (PF) based on Monte Carlo sampling algorithms for tackling strong nonlinear and non-Gaussian models. Considering the aircraft engine is a complicated machine, operating under a harsh environment, and polluted by complex noises, the PF might be an available way to monitor gas path health for aircraft engines. Up to this point in time a number of Kalman filtering approaches have been used for aircraft turbofan engine gas path health estimation, but the particle filters have not been used for this purpose and a systematic comparison has not been published. This paper presents gas path health monitoring based on the PF and the constrained extend Kalman particle filter (cEKPF), and then compares the estimation accuracy and computational effort of these filters to the EKF for aircraft engine performance estimation under rapid faults and general deterioration. Finally, the effects of the constraint mechanism and particle number on the cEKPF are discussed. We show in this paper that the cEKPF outperforms the EKF, PF and EKPF, and conclude that the cEKPF is the best choice for turbofan engine health monitoring.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:1:p:492-513:d:22922
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    References listed on IDEAS

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    1. Li, Y.G. & Pilidis, P., 2010. "GA-based design-point performance adaptation and its comparison with ICM-based approach," Applied Energy, Elsevier, vol. 87(1), pages 340-348, January.
    2. 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.
    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.
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    Cited by:

    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. 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.
    3. 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.
    4. 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.
    5. 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.
    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. 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.
    8. 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).
    9. 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.
    10. 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.
    11. 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.

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