Development of a method for optimizing the structure of static neural networks intended for categorizing technical state of gas-turbine engines

Authors

DOI:

https://doi.org/10.15587/1729-4061.2020.218137

Keywords:

static neural network, gas turbine engine, activation function, hyperbolic tangent

Abstract

A process of creating a static neural network intended for diagnosing bypass gas turbine aircraft engines by a method of categorizing the technical state of the engine flow path was considered. Diagnostics depth was "to the structural assembly". A variant of diagnosing single faults of the flow path was considered.

The following tasks were set:

‒ select the best neuron activation functions in the network layers;

‒ determine the number of layers;

‒ determine the optimal number of neurons in layers;

‒ determine the optimal size of the training set.

The problem was solved taking into account the influence of parameter measurement errors.

The method of structure optimization implies training the network of the selected configuration using a training data set. The training was periodically interrupted to analyze the results of the network operation according to the criterion characterizing the quality of classification of the engine technical state. The assessment was performed with training and control sets. The network that provides the best value of the classification quality parameter assessed by the test set was selected as the final network.

The PS-90A turbojet engine was selected as the object of diagnostics. Diagnostics was carried out on takeoff mode and during the initial climb.

Primary optimization was carried out according to the data with no measurement errors. It was shown that a two-layer network with the use of neurons having a hyperbolic tangent function in both layers is sufficient to solve the problem. The size of the first network layer was finally optimized according to the data containing measurement errors. A two-layer network with eight neurons in the first layer was obtained. The share of erroneous diagnoses measured 14.5 %

Author Biographies

Oleksandr Yakushenko, National Aviation University Liubomyra Huzara ave., 1, Kyiv, Ukraine, 03058

PhD, Associate Professor, Senior Researcher

Department of Aviation Engines

Oleksandr Popov, National Aviation University Liubomyra Huzara ave., 1, Kyiv, Ukraine, 03058

PhD, Associate Professor

Department of Aircraft Continuing Airworthiness

Azer Mirzoyev, National Academy of Aviation Bina highway, 25, Baku, Azerbaijan, AZ1045

PhD, Senior Researcher

Department of Flying Machines and Aviation Engines

Oleg Chumak, TOV Aviaremontne Pidpryiemstvo URARP Polova str., 37, Kyiv, Ukraine, 03056

Deputy Director

Valerii Okhmakevych, National Aviation University Liubomyra Huzara ave., 1, Kyiv, Ukraine, 03058

Researcher

Department of Aviation Engines

References

  1. Sutskever, I., Martens, J., Dahl, G., Hinton, G. (2013). On the importance of initialization and momentum in deep learning. Proceedings of the 30th International Conference on Machine Learning, 28 (3), 1139–1147. Available at: http://proceedings.mlr.press/v28/sutskever13.html
  2. Krutikov, V. N., Samoylenko, N. S. (2018). On the convergence rate of the subgradient method with metric variation and its applications in neural network approximation schemes. Vestnik Tomskogo Gosudarstvennogo Universiteta. Matematika i Mekhanika, 55, 22–37. doi: https://doi.org/10.17223/19988621/55/3
  3. Brownlee, J. (2018). How to Avoid Overfitting in Deep Learning Neural Networks. Available at: https://machinelearningmastery.com/introduction-to-regularization-to-reduce-overfitting-and-improve-generalization-error/
  4. Denil, M., Shakibi, B., Dinh, L., Ranzato, M., De Freitas, N. (2014). Predicting Parameters in Deep Learning. arXiv.org. Available at: https://arxiv.org/pdf/1306.0543.pdf
  5. Han, S., Pool, J., Tran, J., Dally, W. J. (2015). Learning both Weights and Connections for Efficient Neural Networks. arXiv.org. Available at: https://arxiv.org/pdf/1506.02626v3.pdf
  6. Patrick, E. A. (1972). Fundamentals of pattern recognition. Prentice-Hall, Inc., 504.
  7. Patrik, E.; Levin, B. (Ed.) (1980). Osnovy teorii raspoznavaniya obrazov. Moscow: Sov. radio, 408. Available at: http://padaread.com/?book=19276
  8. Rashid, T. (2016). Make your own neural network. CreateSpace, 222.
  9. Medvedev, V. S., Potemkin, V. G.; Potemkin, V. G. (Ed.) (2002). Neyronnye seti. MATLAB 6. Moscow: DIALOG-MIFI, 496.
  10. Medvedev, V. S., Potemkin, V. G. (2001). Neyronnye seti. MATLAB 6. Pakety prikladnyh programm. Kn. 4. Moscow: DIALOG-MIFI, 630.
  11. Lo Gatto, E., Li, Y. G., Pilidis, P. (2006). Gas Turbine Off-Design Performance Adaptation Using a Genetic Algorithm. Volume 2: Aircraft Engine; Ceramics; Coal, Biomass and Alternative Fuels; Controls, Diagnostics and Instrumentation; Environmental and Regulatory Affairs. doi: https://doi.org/10.1115/gt2006-90299
  12. Sampath, S., Ogaji, S., Singh, R., Probert, D. (2002). Engine-fault diagnostics: an optimisation procedure. Applied Energy, 73 (1), 47–70. doi: https://doi.org/10.1016/s0306-2619(02)00051-x
  13. Li, Y. G. (2002). Performance-analysis-based gas turbine diagnostics: A review. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 216 (5), 363–377. doi: https://doi.org/10.1243/095765002320877856
  14. 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
  15. Ross, T. J. (2010). Fuzzy logic with engineering applications. John Wiley & Sons. doi: https://doi.org/10.1002/9781119994374
  16. Yang, M., Shen, Q. (2008). Reinforcing fuzzy rule-based diagnosis of turbomachines with case-based reasoning. International Journal of Knowledge-Based and Intelligent Engineering Systems, 12 (2), 173–181. doi: https://doi.org/10.3233/kes-2008-12208
  17. Ogaji, S. O. T., Li, Y. G., Sampath, S., Singh, R. (2003). Gas Path Fault Diagnosis of a Turbofan Engine From Transient Data Using Artificial Neural Networks. Volume 1: Turbo Expo 2003. doi: https://doi.org/10.1115/gt2003-38423
  18. Angeli, C., Chatzinikolaou, A. (2004). On-Line Fault Detection Techniques for Technical Systems: A Survey. International Journal of Computer Science & Applications, I (1), 12–30. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.100.6189&rep=rep1&type=pdf
  19. Agneli, C. (2010). Chap. 4. Diagnostic Expert Systems: From Expert’s Knowledge to Real-Time Systems. Vol. 1. TMRF e-Book, Advanced Knowledge Based Systems: Model, Applications & Research, 1, 50–73. Available at: http://www.tmrfindia.org/eseries/ebookv1-c4.pdf
  20. Asgari, H., Chen, X. (2015). Gas Turbines Modeling, Simulation, and Control. CRC Press, 206. doi: https://doi.org/10.1201/b18956
  21. Osigwe, E., Li, Y.-G., Suresh, S., Jombo, G., Indarti, D. (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. 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
  22. Loboda, I. (2010). Gas Turbine Condition Monitoring and Diagnostics. Gas Turbines, 119–144. doi: https://doi.org/10.5772/10210
  23. Loboda, I., Feldshteyn, Y., Ponomaryov, V. (2012). Neural Networks for Gas Turbine Fault Identification: Multilayer Perceptron or Radial Basis Network? International Journal of Turbo & Jet-Engines, 29 (1). doi: https://doi.org/10.1515/tjj-2012-0005
  24. Nihmakin, M. A., Zal'tsman, M. M. (1997). Konstruktsiya osnovnyh uzlov dvigatelya PS-90A. Perm', 92.
  25. Kulyk, M., Abdullayev, P., Yakushenko, O., Popov, O., Mirzoyev, A., Chumak, O., Okhmakevych, V. (2018). Development of a data acquisition method to train neural networks to diagnose gas turbine engines and gas pumping units. Eastern-European Journal of Enterprise Technologies, 6 (9 (96)), 55–63. doi: https://doi.org/10.15587/1729-4061.2018.147720
  26. Kulyk, M., Dmitriev, S., Yakushenko, O., Popov, O. (2013). Method of formulating input parameters of neural network for diagnosing gas-turbine engines. Aviation, 17 (2), 52–56. doi: https://doi.org/10.3846/16487788.2013.805868
  27. Hagan, M. T., Menhaj, M. B. (1994). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5 (6), 989–993. doi: https://doi.org/10.1109/72.329697
  28. Wilamowski, B. M., Yu, H. (2010). Improved Computation for Levenberg-Marquardt Training. IEEE Transactions on Neural Networks, 21 (6), 930–937. doi: https://doi.org/10.1109/tnn.2010.2045657
  29. Wilamowski, B. M., Irwin, J. D. (Eds.) (2018). Intelligent Systems. CRC Press, 610. doi: https://doi.org/10.1201/9781315218427
  30. ANFIS. Available at: https://ru.m.wikipedia.org/wiki/ANFIS
  31. Popov, A. V. (2007). Issledovanie dinamicheskih harakteristik TRDD s peremezhayushchimisya neispravnostyami protochnoy chasti na ustanovivshihsya rezhimah ego raboty. Aviatsionno-kosmicheskaya tehnika i tehnologiya, 2 (38), 63–67.

Downloads

Published

2020-12-31

How to Cite

Yakushenko, O., Popov, O., Mirzoyev, A., Chumak, O., & Okhmakevych, V. (2020). Development of a method for optimizing the structure of static neural networks intended for categorizing technical state of gas-turbine engines. Eastern-European Journal of Enterprise Technologies, 6(9 (108), 53–62. https://doi.org/10.15587/1729-4061.2020.218137

Issue

Vol. 6 No. 9 (108) (2020): Information and controlling system

Section

Information and controlling system

License

Copyright (c) 2020 Oleksandr Yakushenko, Oleksandr Popov, Azer Mirzoyev, Oleg Chumak, Valerii Okhmakevych

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.