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A time-series turbofan engine successive fault diagnosis under both steady-state and dynamic conditions

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  • Chen, Yu-Zhi
  • Tsoutsanis, Elias
  • Wang, Chen
  • Gou, Lin-Feng

Abstract

In recent years there has been a growing interest in gas turbine fault diagnosis, especially under dynamic conditions, due to the evolving operating profile of gas turbines and the need to deploy computationally efficient and high-precision diagnostic solutions in real-time. One of the main challenges of fault diagnosis in real-time is the power imbalance between the compressor and turbine that occurs during transient operation. In addition, the heat soakage phenomenon characterizing the transient conditions has a substantial impact on the accuracy of the diagnosis. Finally, any sudden failure that might happen during transient operating conditions creates an additional challenge to fault diagnostics. The present study proposes a gas turbine diagnostic approach based on time-series measurements encapsulating steady-state and transient operating conditions. Specifically, the introduced novel approach is capable of quantifying the surplus/deficit of the power between the compressor and the turbine by utilizing the time-series data representing the observed deviations in the shaft rotational speed in order to determine the power balance in the shaft. The maximum diagnostic errors for constant fault and sudden failure are less than 0.006% during the dynamic maneuver. The results demonstrate and illustrate that the proposed method could effectively and accurately diagnose the severity of aero-engine faults at both steady-state and transient conditions. Therefore, this study has great potential for gas turbine practitioners since the diagnosis under transient conditions in real-time can enhance the capability of engine online condition monitoring and improve the condition-based maintenance of gas turbine assets.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:263:y:2023:i:pd:s0360544222027347
    DOI: 10.1016/j.energy.2022.125848
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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. Palmé, Thomas & Fast, Magnus & Thern, Marcus, 2011. "Gas turbine sensor validation through classification with artificial neural networks," Applied Energy, Elsevier, vol. 88(11), pages 3898-3904.
    3. Kiaee, Mehrdad & Tousi, A.M., 2021. "Vector-based deterioration index for gas turbine gas-path prognostics modeling framework," Energy, Elsevier, vol. 216(C).
    4. Balli, Ozgur & Caliskan, Hakan, 2021. "Turbofan engine performances from aviation, thermodynamic and environmental perspectives," Energy, Elsevier, vol. 232(C).
    5. 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).
    6. 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.
    7. Park, Yeseul & Choi, Minsung & Kim, Kibeom & Li, Xinzhuo & Jung, Chanho & Na, Sangkyung & Choi, Gyungmin, 2020. "Prediction of operating characteristics for industrial gas turbine combustor using an optimized artificial neural network," Energy, Elsevier, vol. 213(C).
    8. Park, Yeseul & Choi, Minsung & Choi, Gyungmin, 2022. "Fault detection of industrial large-scale gas turbine for fuel distribution characteristics in start-up procedure using artificial neural network method," Energy, Elsevier, vol. 251(C).
    9. Ibrahem, Ibrahem M.A. & Akhrif, Ouassima & Moustapha, Hany & Staniszewski, Martin, 2021. "Nonlinear generalized predictive controller based on ensemble of NARX models for industrial gas turbine engine," Energy, Elsevier, vol. 230(C).
    10. Chen, Yu-Zhi & Li, Yi-Guang & Newby, Mike A., 2019. "Performance simulation of a parallel dual-pressure once-through steam generator," Energy, Elsevier, vol. 173(C), pages 16-27.
    11. Tsoutsanis, Elias & Meskin, Nader, 2017. "Derivative-driven window-based regression method for gas turbine performance prognostics," Energy, Elsevier, vol. 128(C), pages 302-311.
    12. 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.
    13. 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).
    14. Wei, Zhiyuan & Zhang, Shuguang & Jafari, Soheil & Nikolaidis, Theoklis, 2022. "Self-enhancing model-based control for active transient protection and thrust response improvement of gas turbine aero-engines," Energy, Elsevier, vol. 242(C).
    15. 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).
    16. 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.
    17. Collins, Jeffrey M. & McLarty, Dustin, 2020. "All-electric commercial aviation with solid oxide fuel cell-gas turbine-battery hybrids," Applied Energy, Elsevier, vol. 265(C).
    18. Mohammadian, Poorya Keshavarz & Saidi, Mohammad Hassan, 2019. "Simulation of startup operation of an industrial twin-shaft gas turbine based on geometry and control logic," Energy, Elsevier, vol. 183(C), pages 1295-1313.
    19. Seyam, Shaimaa & Dincer, Ibrahim & Agelin-Chaab, Martin, 2021. "Investigation of two hybrid aircraft propulsion and powering systems using alternative fuels," Energy, Elsevier, vol. 232(C).
    20. Sun, Rongzhuo & Shi, Licheng & Yang, Xilian & Wang, Yuzhang & Zhao, Qunfei, 2020. "A coupling diagnosis method of sensors faults in gas turbine control system," Energy, Elsevier, vol. 205(C).
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