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Prospects for aero gas-turbine diagnostics: a review

Author

Listed:
  • Marinai, Luca
  • Probert, Douglas
  • Singh, Riti

Abstract

Despite inflating unit-fuel costs, the long-term prospects for the aircraft industry remain buoyant. Nevertheless reducing direct operating-costs is crucial to ensure competitive advantages for airlines and manufacturers, and so effective advanced engine-condition monitoring methodologies are desirable. Hence gas-path diagnostic methods are reviewed and the specifications for such effective tools deduced, together with pertinent future prospects.

Suggested Citation

  • Marinai, Luca & Probert, Douglas & Singh, Riti, 2004. "Prospects for aero gas-turbine diagnostics: a review," Applied Energy, Elsevier, vol. 79(1), pages 109-126, September.
    • Handle: RePEc:eee:appene:v:79:y:2004:i:1:p:109-126
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    Citations

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    Cited by:

    1. Muhammad Baqir Hashmi & Mohammad Mansouri & Amare Desalegn Fentaye & Shazaib Ahsan & Konstantinos Kyprianidis, 2024. "An Artificial Neural Network-Based Fault Diagnostics Approach for Hydrogen-Fueled Micro Gas Turbines," Energies, MDPI, vol. 17(3), pages 1-23, February.
    2. Nalianda, D.K. & Kyprianidis, K.G. & Sethi, V. & Singh, R., 2015. "Techno-economic viability assessments of greener propulsion technology under potential environmental regulatory policy scenarios," Applied Energy, Elsevier, vol. 157(C), pages 35-50.
    3. Fei Li & Hongzhi Wang & Guowen Zhou & Daren Yu & Jiangzhong Li & Hong Gao, 2017. "Anomaly Detection in Gas Turbine Fuel Systems Using a Sequential Symbolic Method," Energies, MDPI, vol. 10(5), pages 1-22, May.
    4. Kyprianidis, Konstantinos G. & Dahlquist, Erik, 2017. "On the trade-off between aviation NOx and energy efficiency," Applied Energy, Elsevier, vol. 185(P2), pages 1506-1516.
    5. Yamamoto, Satoru & Uemura, Akihiro & Miyazawa, Hironori & Furusawa, Takashi & Yonezawa, Koichi & Umezawa, Shuichi & Ohmori, Shuichi & Suzuki, Takeshi, 2020. "A numerical and analytical coupling method for predicting the performance of intermediate-pressure steam turbines in operation," Energy, Elsevier, vol. 198(C).
    6. Kang, Do Won & Kim, Tong Seop, 2018. "Model-based performance diagnostics of heavy-duty gas turbines using compressor map adaptation," Applied Energy, Elsevier, vol. 212(C), pages 1345-1359.
    7. Maria Grazia De Giorgi & Nicola Menga & Antonio Ficarella, 2023. "Exploring Prognostic and Diagnostic Techniques for Jet Engine Health Monitoring: A Review of Degradation Mechanisms and Advanced Prediction Strategies," Energies, MDPI, vol. 16(6), pages 1-37, March.
    8. 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.
    9. 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.
    10. Dong, Keqiang & Long, Linan & Zhang, Hong & Gao, You, 2018. "The mutual information based minimum spanning tree to detect and evaluate dependencies between aero-engine gas path system variables," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 248-253.
    11. 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).

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