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A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions

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  • Chen, Yu-Zhi
  • Tsoutsanis, Elias
  • Xiang, Heng-Chao
  • Li, Yi-Guang
  • Zhao, Jun-Jie

Abstract

At present, aero engine fault diagnosis is mainly based on the steady-state condition at the cruise phase, and the gas path parameters in the entire flight process are not effectively used. At the same time, high quality steady-state monitoring measurements are not always available and as a result the accuracy of diagnosis might be affected. There is a recognized need for real-time performance diagnosis of aero engines operating under transient conditions, which can improve their condition-based maintenance. Recent studies have demonstrated the capability of the sequential model-based diagnostic method to predict accurately and efficiently the degradation of industrial gas turbines under steady-state conditions. Nevertheless, incorporating real-time data for fault detection of aero engines that operate in dynamic conditions is a more challenging task. The primary objective of this study is to investigate the performance of the sequential diagnostic method when it is applied to aero engines that operate under transient conditions while there is a variation in the bypass ratio and the heat soakage effects are taken into consideration. This study provides a novel approach for quantifying component degradation, such as fouling and erosion, by using an adapted version of the sequential diagnostic method. The research presented here confirms that the proposed method could be applied to aero engine fault diagnosis under both steady-state and dynamic conditions in real-time. In addition, the economic impact of engine degradation on fuel cost and payload revenue is evaluated when the engine under investigation is using hydrogen. The proposed method demonstrated promising diagnostic results where the maximum prediction errors for steady state and transient conditions are less than 0.006% and 0.016%, respectively. The comparison of the proposed method to a benchmark diagnostic method revealed a 15% improvement in accuracy which can have great benefit when considering that the cost attributed to degradation can reach up to $702,585 for 6000 flight cycles of a hydrogen powered aircraft fleet. This study provides an opportunity to improve our understanding of aero engine fault diagnosis in order to improve engine reliability, availability, and efficiency by online health monitoring.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:317:y:2022:i:c:s0306261922005232
    DOI: 10.1016/j.apenergy.2022.119148
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    References listed on IDEAS

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    1. Chen, Yu-Zhi & Zhao, Xu-Dong & Xiang, Heng-Chao & Tsoutsanis, Elias, 2021. "A sequential model-based approach for gas turbine performance diagnostics," Energy, Elsevier, vol. 220(C).
    2. Ogaji, Stephen & Sampath, Suresh & Singh, Riti & Probert, Douglas, 2002. "Novel approach for improving power-plant availability using advanced engine diagnostics," Applied Energy, Elsevier, vol. 72(1), pages 389-407, May.
    3. Zhang, Jiyuan & Tang, Hailong & Chen, Min, 2019. "Linear substitute model-based uncertainty analysis of complicated non-linear energy system performance (case study of an adaptive cycle engine)," Applied Energy, Elsevier, vol. 249(C), pages 87-108.
    4. Kim, Min Jae & Kim, Tong Seop & Flores, Robert J. & Brouwer, Jack, 2020. "Neural-network-based optimization for economic dispatch of combined heat and power systems," Applied Energy, Elsevier, vol. 265(C).
    5. Tsoutsanis, Elias & Meskin, Nader & Benammar, Mohieddine & Khorasani, Khashayar, 2016. "A dynamic prognosis scheme for flexible operation of gas turbines," Applied Energy, Elsevier, vol. 164(C), pages 686-701.
    6. Kim, Sangjo & Son, Changmin & Kim, Kuisoon, 2017. "Combining effect of optimized axial compressor variable guide vanes and bleed air on the thermodynamic performance of aircraft engine system," Energy, Elsevier, vol. 119(C), pages 199-210.
    7. Zhang, Han & Wang, Liang & Lin, Xipeng & Chen, Haisheng, 2020. "Combined cooling, heating, and power generation performance of pumped thermal electricity storage system based on Brayton cycle," Applied Energy, Elsevier, vol. 278(C).
    8. Plis, Marcin & Rusinowski, Henryk, 2018. "A mathematical model of an existing gas-steam combined heat and power plant for thermal diagnostic systems," Energy, Elsevier, vol. 156(C), pages 606-619.
    9. Tsoutsanis, Elias & Meskin, Nader, 2017. "Derivative-driven window-based regression method for gas turbine performance prognostics," Energy, Elsevier, vol. 128(C), pages 302-311.
    10. Hu, R.L. & Granderson, J. & Auslander, D.M. & Agogino, A., 2019. "Design of machine learning models with domain experts for automated sensor selection for energy fault detection," Applied Energy, Elsevier, vol. 235(C), pages 117-128.
    11. 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).
    12. Safiyullah, F. & Sulaiman, S.A. & Naz, M.Y. & Jasmani, M.S. & Ghazali, S.M.A., 2018. "Prediction on performance degradation and maintenance of centrifugal gas compressors using genetic programming," Energy, Elsevier, vol. 158(C), pages 485-494.
    13. 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).
    14. 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.
    15. Chao, Ching-Cheng & Li, Ru-Guo, 2017. "Effects of cargo types and load efficiency on airline cargo revenues," Journal of Air Transport Management, Elsevier, vol. 61(C), pages 26-33.
    16. Wang, Liang & Lin, Xipeng & Zhang, Han & Peng, Long & Chen, Haisheng, 2021. "Brayton-cycle-based pumped heat electricity storage with innovative operation mode of thermal energy storage array," Applied Energy, Elsevier, vol. 291(C).
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    1. 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).
    2. Irani, Fatemeh Negar & Soleimani, Mohammadjavad & Yadegar, Meysam & Meskin, Nader, 2024. "Deep transfer learning strategy in intelligent fault diagnosis of gas turbines based on the Koopman operator," Applied Energy, Elsevier, vol. 365(C).
    3. Chen, Yu-Zhi & Tsoutsanis, Elias & Wang, Chen & Gou, Lin-Feng, 2023. "A time-series turbofan engine successive fault diagnosis under both steady-state and dynamic conditions," Energy, Elsevier, vol. 263(PD).
    4. Zhao, Junjie & Li, Yi-Guang & Sampath, Suresh, 2023. "A hierarchical structure built on physical and data-based information for intelligent aero-engine gas path diagnostics," Applied Energy, Elsevier, vol. 332(C).
    5. Huang, Yufeng & Tao, Jun & Zhao, Junyi & Sun, Gang & Yin, Kai & Zhai, Junyi, 2023. "Graph structure embedded with physical constraints-based information fusion network for interpretable fault diagnosis of aero-engine," Energy, Elsevier, vol. 283(C).

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