Development of a data acquisition method to train neural networks to diagnose gas turbine engines and gas pumping units
Keywords:діагностування, нейронна мережа, навчальна множина, контрольна множина, газотурбінний, газоперекачувальний
The application of neural networks is one of promising ways to improve efficiency when diagnosing aviation gas turbine engines and gas pumping units. In order to start functioning of such network, it should be trained first using the pre-defined training sets. These data should fully characterize work of the object in a wide range of operating modes and at various technical states of the diagnosticated assemblies. In addition, it is necessary to have a similar data set to monitor quality of the neural network learning.
To train the network to recognize faults of one type, a set of 20‒200 or more training examples is required. Obtaining such information in operation or in full-scale tests is a rather long or costly process.
A method for acquisition of training and control data sets was proposed. The sets are intended to train static neural networks recognizing single and multiple faults of the elements of air-gas channels of gas turbine engines and gas pumping units. The method enables obtaining sets of working process parameters describing operation of objects at various technical states of an air-gas channel, effect of measurement errors and object functioning in a wide range of modes and external conditions. Composition of the pumped gas is additionally taken into account for gas pumping units.
To obtain the required parameters, a mathematical model of the working process of the object of the second level of complexity was used.
The sets characterize work of operable objects and objects with significant malfunctions in spools of compressors and turbines and in a combustion chamber and for the case of a gas pumping unit, in its supercharger.Two variants of formation of sets were considered: using the measured parameters of the working process; deviations of the measured parameters from their reference values and the parameters used as regime parameters in the mathematical model of the working process. For the second variant, check of expediency of including the regime parameters in the sets was made. It has been shown that regime parameters can be excluded from data sets in some cases
- Patan, K. (2008). Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes. Springer. doi: https://doi.org/10.1007/978-3-540-79872-9
- Medvedev, V. S., Potemkin, V. G. (2002). Neyronnye seti. Matlab 6. Moscow: DIALOG-MIFI, 496.
- Osigwe, E., Yi-Guang, L., Sampath, S., Gbanaibolou, J., Dieni, I. (2017). Integrated Gas Turbine System Diagnostics: Components and Sensor Faults Quantification using Artificial Neural Network. 23rd International Society of Air Breathing Engines (ISABE) Conference – ISABE 2017. Manchester. Available at: https://www.researchgate.net/profile/Emmanuel_Osigwe/publication/319645027_Integrated_Gas_Turbine_System_Diagnostics_Components_and_Sensor_Faults_Quantification_using_Artificial_Neural_Network/links/59e52ae90f7e9b0e1aa888f0/Integrated-Gas-Turbine-System-Diagnostics-Components-and-Sensor-Faults-Quantification-using-Artificial-Neural-Network.pdf?origin=publication_detail
- Loboda, I. (2010). Gas Turbine Condition Monitoring and Diagnostics. Gas Turbines, 119–144. doi: https://doi.org/10.5772/10210
- Loboda, I., Feldshteyn, Y., Ponomaryov, V. (2012). Neural Networks for Gas Turbine Fault Identification: Multilayer Perceptron or Radial Basis Network? Int. J. Turbo Jet-Engines, 29 (1), 37–48. doi: https://doi.org/10.1515/tjj-2012-0005
- Ismail, R. I. B., Ismail Alnaimi, F. B., AL-Qrimli, H. F. (2016). Artificial Intelligence Application in Power Generation Industry: Initial considerations. IOP Conference Series: Earth and Environmental Science, 32 (1), 012007. doi: https://doi.org/10.1088/1755-1315/32/1/012007
- Kucher, A. G., Dmitriev, S. A., Popov, A. V. (2007). Opredelenie tekhnicheskogo sostoyaniya TRDD po dannym eksperimental'nyh issledovaniy s ispol'zovaniem neyronnyh setey i metodov raspoznavaniya obrazov. Aviacionno-kosmicheskaya tekhnika i tekhnologiya, 10 (46), 153–164. Available at: http://nbuv.gov.ua/UJRN/aktit_2007_10_34
- Pat. No. 2445598C1 RU. Diagnostic method of technical state of gas-turbine engine (2010). No. RU2010134067A; declareted: 13.08.2010; published: 20.03.2012, Bul. No. 8. URL: http://www.freepatent.ru/patents/2445598
- Yildirim, M. T., Kurt, B. (2018). Aircraft Gas Turbine Engine Health Monitoring System by Real Flight Data. International Journal of Aerospace Engineering, 2018, 1–12. doi: https://doi.org/10.1155/2018/9570873
- Pérez-Ruiz, J. L., Loboda, I., Miró-Zárate, L. A., Toledo-Velázquez, M., Polupan, G. (2017). Evaluation of gas turbine diagnostic techniques under variable fault conditions. Advances in Mechanical Engineering, 9 (10), 168781401772747. doi: https://doi.org/10.1177/1687814017727471
- Ntantis, E. L., Botsaris, P. N. (2015). Diagnostic Methods for an Aircraft Engine Performance. Journal of Engineering Science and Technology Review, 8 (4), 64–72. doi: https://doi.org/10.25103/jestr.084.10
- Nyulászi, L., Andoga, R., Butka, P., Főző, L., Kovacs, R., Moravec, T. (2014). Fault Detection and Isolation of an Aircraft Turbojet Engine Using a Multi-Sensor Network and Multiple Model Approach. Acta Polytechnica Hungarica, 15 (2), 189–209. doi: https://doi.org/10.12700/aph.15.1.2018.2.10
- Ahmedzyanov, A. M., Dubravskiy, N. G., Tunakov, A. P. (1983). Diagnostika sostoyaniya VRD po termogazodinamicheskim parametram. Moscow: Mashinostroenie, 206.
- Yakushenko, O. S., Korolyov, P. V., Miltsov, V. E., Chumak, O. I., Ohmakevich, V. M. (2014). Identification of aviation gas turbine engine mathematical model by operational data. Visnyk dvyhunobuduvannia, 2, 130–138. Available at: http://nbuv.gov.ua/UJRN/vidv_2014_2_23
- Popov, A. V., Stepushkina, E. P., Slepuhina, I. A. (2007). Eksperimental'noe issledovanie harakteristik TRDD pri peremezhayushchihsya povrezhdeniyah protochnoy chasti. Materialy VIII Mizhnarodnoi naukovo-tekhnichnoi konferentsiyi „AVIA – 2007”. Vol. 2. Kyiv: NAU, 33.37–33.40. Available at: http://er.nau.edu.ua:8080/handle/NAU/36929
- Rozghoniuk, V. V., Rudnik, A. A., Kolomieiev, V. M., Hryhil, M. A., Molokan, O. O.; Rudnyk, A. A. (Ed.) (2001). Dovidnyk pratsivnyka hazotransportnoho pidpryiemstva. Kyiv: «Rostok», 1092.
- Nihmakin, M. A., Zal'cman, M. M. (1997). Konstrukciya osnovnyh uzlov dvigatelya PS-90A. Perm', 92.
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Copyright (c) 2018 Mykola Kulyk, Parviz Abdullayev, Oleksandr Yakushenko, Oleksandr Popov, Azer Mirzoyev, Oleg Chumak, Valerii Okhmakevych
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