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Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter

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  • Feng Lu

    (Jiangsu Province Key Laboratory of Aerospace Power Systems, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
    Collaborative Innovation Center of Advanced Aero-Engine, Beijing 100191, China)

  • Yafan Wang

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

  • Jinquan Huang

    (Jiangsu Province Key Laboratory of Aerospace Power Systems, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
    Collaborative Innovation Center of Advanced Aero-Engine, Beijing 100191, China)

  • Yihuan Huang

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

Abstract

This paper considers the problem of gas turbine transient performance tracking in a cluttered environment. To increase the accuracy and robustness of state estimation, a data-fusion nonlinear estimation method based on an adaptive particle filter (PF) is proposed. This method needs local estimates transmitted to a central filtering unit for data fusion, and then global data feedback to the local PF for consensus propagation. The computational burden is shared by the local PF and central filtering unit in the data-fusion architecture. Furthermore, the PF algorithm used for the data fusion is embedded with the prior knowledge of engine health condition and adaptive to the measurement noise, and hence is called the adaptive PF. The heuristic information of state variables represented by inequality constraints tunes the local estimates by a probability density truncation method. The covariance of measurement noise is calculated by wavelet transform and utilized to update the particle importance function of the real time PF. The performance improvements of the proposed method are indicated through extensive experiments for gradual and abrupt shift performance tracking under conditions of gas turbine transient operation.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:12:p:12403-13927:d:60180
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    References listed on IDEAS

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    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.
    2. 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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    Citations

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    Cited by:

    1. 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.
    2. Valentina Zaccaria & Moksadur Rahman & Ioanna Aslanidou & Konstantinos Kyprianidis, 2019. "A Review of Information Fusion Methods for Gas Turbine Diagnostics," Sustainability, MDPI, vol. 11(22), pages 1-20, November.
    3. 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.
    4. Xu, Maojun & Liu, Jinxin & Li, Ming & Geng, Jia & Wu, Yun & Song, Zhiping, 2022. "Improved hybrid modeling method with input and output self-tuning for gas turbine engine," Energy, Elsevier, vol. 238(PA).
    5. 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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