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Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

Author

Listed:
  • Martí de Castro-Cros

    (Intelligent Data Science and Artificial Intelligence Research Centre (IDEAI), Automatic Control Department, Universitat Politècnica de Catalunya, Campus Nord, Jordi Girona, 1-3, 08034 Barcelona, Spain)

  • Manel Velasco

    (Intelligent Data Science and Artificial Intelligence Research Centre (IDEAI), Automatic Control Department, Universitat Politècnica de Catalunya, Campus Nord, Jordi Girona, 1-3, 08034 Barcelona, Spain)

  • Cecilio Angulo

    (Intelligent Data Science and Artificial Intelligence Research Centre (IDEAI), Automatic Control Department, Universitat Politècnica de Catalunya, Campus Nord, Jordi Girona, 1-3, 08034 Barcelona, Spain)

Abstract

Condition monitoring, diagnostics, and prognostics are key factors in today’s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machine-learning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8468-:d:702954
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    References listed on IDEAS

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    Cited by:

    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. Ayman AboElHassan & Soumaya Yacout, 2023. "A digital shadow framework using distributed system concepts," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3579-3598, December.
    3. Jacek Czyżewicz & Piotr Jaskólski & Paweł Ziemiański & Marian Piwowarski & Mateusz Bortkiewicz & Krzysztof Laszuk & Ireneusz Galara & Marta Pawłowska & Karol Cybulski, 2022. "Towards Designing an Innovative Industrial Fan: Developing Regression and Neural Models Based on Remote Mass Measurements," Energies, MDPI, vol. 15(7), pages 1-19, March.
    4. Nicola Menga & Akhila Mothakani & Maria Grazia De Giorgi & Radoslaw Przysowa & Antonio Ficarella, 2022. "Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine," Energies, MDPI, vol. 15(19), pages 1-22, October.
    5. Volodymyr Grudz & Yaroslav Grudz & Ivan Pavlenko & Oleksandr Liaposhchenko & Marek Ochowiak & Vasyl Pidluskiy & Oleksandr Portechyn & Mykola Iakymiv & Sylwia Włodarczak & Andżelika Krupińska & Magdale, 2023. "Ensuring the Reliability of Gas Supply Systems by Optimizing the Overhaul Planning," Energies, MDPI, vol. 16(2), pages 1-13, January.

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