Machine-Learning-Based Condition Assessment of Gas Turbines—A Review
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Cited by:
- 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).
- 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.
- 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.
- 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.
- 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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Keywords
artificial intelligence; machine learning; soft sensor; condition assessment; gas turbine;All these keywords.
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