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Surrogate model-based real-time gas path fault diagnosis for gas turbines under transient conditions

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  • Cheng, Xianda
  • Zheng, Haoran
  • Yang, Qian
  • Zheng, Peiying
  • Dong, Wei

Abstract

Advanced diagnostic algorithms and high-fidelity simulation models improve the accuracy of model-based gas path fault diagnosis for gas turbines (GTs). But simultaneously, it becomes difficult in real-time applications due to the increased calculation amount. To improve the diagnosis speed, this study adopts the surrogate method to realize the real-time gas path fault diagnosis of GTs under transient operating conditions. First, the component level model (CLM) is built and verified. Subsequently, the surrogate model is established by combining the artificial neural network (ANN) and the necessary physical model. The constructed surrogate model can almost entirely reproduce the simulation results of CLM under the whole operation conditions. Finally, the real-time fault diagnosis system combines the surrogate model and the unscented Kalman filter (UKF). The results show that the surrogate model-based fault diagnosis system has the same accuracy as the CLM-based system. At the same time, the calculation speed is increased by nearly 50 times, which meets the real-time requirements of fault diagnosis.

Suggested Citation

  • Cheng, Xianda & Zheng, Haoran & Yang, Qian & Zheng, Peiying & Dong, Wei, 2023. "Surrogate model-based real-time gas path fault diagnosis for gas turbines under transient conditions," Energy, Elsevier, vol. 278(PA).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:pa:s0360544223013385
    DOI: 10.1016/j.energy.2023.127944
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    References listed on IDEAS

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    1. Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Liu, Jiao & Yu, Daren, 2021. "Convolutional neural network-based deep transfer learning for fault detection of gas turbine combustion chambers," Applied Energy, Elsevier, vol. 302(C).
    2. Kim, Sangjo & Kim, Kuisoon & Son, Changmin, 2020. "A new transient performance adaptation method for an aero gas turbine engine," Energy, Elsevier, vol. 193(C).
    3. Martí de Castro-Cros & Manel Velasco & Cecilio Angulo, 2021. "Machine-Learning-Based Condition Assessment of Gas Turbines—A Review," Energies, MDPI, vol. 14(24), pages 1-27, December.
    4. Kim, Min Jae & Kim, Tong Seop, 2019. "Integration of compressed air energy storage and gas turbine to improve the ramp rate," Applied Energy, Elsevier, vol. 247(C), pages 363-373.
    5. Cheng, Xianda & Zheng, Haoran & Dong, Wei & Yang, Xuesen, 2023. "Performance prediction of marine intercooled cycle gas turbine based on expanded similarity parameters," Energy, Elsevier, vol. 265(C).
    6. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    7. Zhou, Dengji & Yao, Qinbo & Wu, Hang & Ma, Shixi & Zhang, Huisheng, 2020. "Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks," Energy, Elsevier, vol. 200(C).
    8. Chen, Yu-Zhi & Tsoutsanis, Elias & Xiang, Heng-Chao & Li, Yi-Guang & Zhao, Jun-Jie, 2022. "A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions," Applied Energy, Elsevier, vol. 317(C).
    9. Kim, Sangjo, 2021. "A new performance adaptation method for aero gas turbine engines based on large amounts of measured data," Energy, Elsevier, vol. 221(C).
    10. Tahan, Mohammadreza & Tsoutsanis, Elias & Muhammad, Masdi & Abdul Karim, Z.A., 2017. "Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review," Applied Energy, Elsevier, vol. 198(C), pages 122-144.
    11. Tsoutsanis, Elias & Meskin, Nader & Benammar, Mohieddine & Khorasani, Khashayar, 2014. "A component map tuning method for performance prediction and diagnostics of gas turbine compressors," Applied Energy, Elsevier, vol. 135(C), pages 572-585.
    12. Lee, Jong Jun & Kang, Do Won & Kim, Tong Seop, 2011. "Development of a gas turbine performance analysis program and its application," Energy, Elsevier, vol. 36(8), pages 5274-5285.
    13. Roy, Rishi & Gupta, Ashwani K., 2022. "Data-driven prediction of flame temperature and pollutant emission in distributed combustion," Applied Energy, Elsevier, vol. 310(C).
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