EVALUATION OF THE TECHNICAL CONDITION OF A GAS TURBINE PLANT USING MACHINE LEARNING METHODS FROM ARTIFICIAL DATA ASSESSING THE TECHNICAL CONDITION OF A GAS TURBINE USING MACHINE LEARNING METHODS WITH ARTIFICIAL DATA
Author:
Blinov Vitalii1, Deryabin Gleb1, Pankrashin Svyatoslav1
Affiliation:
1. Ural Federal University named First President of Russia B. Yeltsin
Abstract
Continuous monitoring of the technical condition of gas turbines, defect identification, failure prevention, and optimization of operation, maintenance, and repair processes are relevant tasks for the operators of this equipment. Various machine learning methods that are already being used in the field of gas turbines can help solve these tasks. The limiting factor in this regard is the lack of real operational data. This study examines the possibility of using synthetic data for training and testing machine learning models to determine the level of technical condition of a gas turbine installation. An open dataset created by other researchers using a mathematical model of a marine gas turbine engine was selected for analysis. The research presents the accuracy values obtained by different methods of evaluating machine learning models. The random forest model demonstrated the best results. It was found that when developing machine learning-based solutions for engineering tasks, additional methods for assessing the accuracy of predictions are required. The further development of this work is associated with the development of a proprietary mathematical model of a gas turbine installation capable of considering the influence of specific defects to create datasets for analysis and further research
Publisher
BSTU named after V.G. Shukhov
Reference11 articles.
1. Roemer M. J., Kacprzynski G. J. Advanced diagnostics and prognostics for gas turbine engine risk assessment //2000 IEEE aerospace conference. proceedings. Т. 6. Big Sky: IEEE, 2000. С. 345-353., Roemer, M. J., & Kacprzynski, G. J. (2000, March). Advanced diagnostics and prognostics for gas tu bine engine risk assessment. IEEE aerospace conference. proceedings (Vol. 6, pp. 345-353). IEEE. http://dx.doi.org/10.1109/AERO.2000.877909 2. Jordan M. I., Mitchell T. M. Machine learning: Trends, perspectives, and prospects //Science. – 2015. Т. 349. №. 6245. С. 255-260., Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415 3. Sun L. et al. Real-time power prediction approach for turbine using deep learning techniques //Energy. 2021. Т. 233. С. 121130., Sun, L., Liu, T., Xie, Y., Zhang, D., & Xia, X. (2021). Real-time power prediction approach for turbine using deep learning techniques. Energy, 233, 121130. http://dx.doi.org/10.1016/j.energy.2021.121130 4. Александров И. В., Дюк В. А., Фомин В. В. Использование методов машинного обучения для определения коэффициента расхода топлива газовой турбины фрегата //Морские интеллектуальные технологии. 2019. № 3-1. С. 156-160., Alexandrov, I. V., Duke, V. A., & Fomin, V. V. (2019). Using machine learning methods to determine the fuel consumption coefficient of a frigate gas turbine [Ispol'zovanie metodov mashinnogo obucheniya dlya opredeleniya koefficienta raskhoda topliva gazovoj turbiny fregata]. Morskiye intellektual'nyye tekhnologii, (3-1), 156-160. https://www.elibrary.ru/lwwupz [In Russian] 5. Coraddu A. et al. Machine learning approaches for improving condition-based maintenance of naval propulsion plants //Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment. 2016. Т. 230, №. 1. С. 136-153., Coraddu, A., Oneto, L., Ghio, A., Savio, S., Anguita, D., & Figari, M. (2016). Machine learning approaches for improving condition-based maintenance of naval propulsion plants. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 230(1), 136-153. https://doi.org/10.1177/1475090214540874
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