NEURAL NETWORK DIAGNOSTICS OF AIRCRAFT PARTS BASED ON THE RESULTS OF OPERATIONAL PROCESSES

Authors

  • S. D. Leoshchenko National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine, Ukraine
  • H. V. Pukhalska National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine, Ukraine
  • S. A. Subbotin National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine, Ukraine
  • A. O. Oliinyk National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukrainе, Ukraine
  • Ye. O. Gofman National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2022-2-7

Keywords:

diagnostics, aviation parts, synthesis, training, neuroevolution, data sampling, operational processes

Abstract

Context. The problem of synthesis of an optimal neural network model for diagnostics of aircraft parts after operational processes is considered. The object of the study is the process of synthesis of neural network diagnostic models for aircraft parts based on the results of operational processes

Objective is to synthesize neural network diagnostic models of aircraft parts after operational processes with a high level of accuracy.

Method. It is proposed to research the use of two approaches to the synthesis of neural network diagnostic models. So, using a system of indicators, the topology of the neural network is calculated, which will be trained using the method of Backpropagation method in the future. The second approach is based on the use of a neuroevolutionary approach, which allows for a complete synthesis of the neural network, dynamically modifying the topology of the solution in addition to the parameters. the final decisions are compared in the accuracy of work on the training and test data set. This approach will allow to determine the possibility and correctness of using neuroevolutionary methods for the synthesis of diagnostic models.

Results. Neuromodels for diagnostics of aircraft parts based on the results of operational processes have been obtained. The obtained results of comparing the methods used for synthesis made it possible to form recommendations for the implementation of neuroevolutionary methods in the synthesis of diagnostic neuromodels.

Conclusions. The results obtained during the experiments confirmed the operability of the mathematical software used and allowed us to form recommendations for further use of the considered methods in practice in order to synthesize diagnostic neuromodels. The prospects for further research may consist in expanding the input data sets in order to synthesize and study more complex topologies of neural network models.

Author Biographies

S. D. Leoshchenko, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine

Post-graduate student of the Department of Software Tools

H. V. Pukhalska, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine

PhD, Associate Professor, Associate Professor of the Department of Machinery Engineering Technology 

S. A. Subbotin, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine

Dr. Sc., Professor, Head of the Department of Software Tools

A. O. Oliinyk, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukrainе

Dr. Sc., Professor, Professor of the Department of Software Tools

Ye. O. Gofman, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine

PhD, Senior Researcher of the Research Unit

References

Smith D. J. Reliability, Maintainability and Risk: Practical Methods for Engineers, Oxford, Butterworth-Heinemann, 2021, 516 p.

Høyer C. B., Nielsen T. S., Nagel L. L., Uhrenholt L., Boel L.W.T. Investigation of a fatal airplane crash: autopsy, computed tomography, and injury pattern analysis used to determine who was steering the plane at the time of the accident. A case report, Forensic Science, Medicine and Pathology, 2012, Vol. 8(2), P. 179–188. DOI: 10.1007/s12024-011-9239-4

Maltry G.W. Airplane Crash Analysis [Electronic resource]. Access mode: https://www.edtengineers.com/blogpost/airplane-crash analysis

Yunusov S., Labendik V., Guseynov S. Monitoring and Diagnostics of Aircraft Gas Turbine Engines: Improvement of Models and Methods for Diagnosis of Gas Path of Gas Turbine Engines, Chisinau: LAP LAMBERT Academic Publishing, 2014, 204 p.

Johri P., Anand A., Vain J., Singh J., Quasim M.T. System Assurances: Modeling and Management (Emerging Methodologies and Applications in Modelling, Identification and Control), Cambridge: Academic Press, 2022, 614 p.

Sun W., Paiva A.R.C., Xu P., Sundaram A., Braatz R.D. Fault Detection and Identification using Bayesian Recurrent Neural Networks, Computers & Chemical Engineering, 2019, Vol. 141, pp. 1–43. DOI: 10.1016/j.compchemeng.2020.106991

Nguyen H.V., Golinval J.-C. Fault detection based on Kernel Principal Component Analysis, Engineering Structures, 2010, Vol. 32(11), pp. 3683–3691. DOI: 10.1016/j.engstruct.2010.08.012

Aldrich C., Auret L. Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods, Berlin: Springer, 2013, 396 p.

Adouni A., Chariag D., Diallo D., Hamed M.B., Sbita L. FDI based on Artificial Neural Network for Low-VoltageRide-Through in DFIG-based Wind Turbine, ISA Transactions, 2016, Vol. 64, pp. 353–364. DOI: 10.1016/j.isatra.2016.05.009

Plikynas D., Akbar Y. H. Neural Network Approaches to Estimating FDI Flows: Evidence from Central and Eastern Europe, Eastern European Economics, Vol. 44, No. 3, 2006, pp. 29–59.

Babenko O., Pribora Т. Analysis of the results of the study of the frequencies and forms of natural vibrations of the working blade of the 1st stage of the SLP, Bulletin of Engine Building, 2018, Vol. 2, pp. 91–98. [In Russian]

Dvirnyk Ya., Pavlenko D. The influence of dust erosion on the gas dynamic characteristics of the axial compressor of the GTE Vestnik dvigatelstroeniya, Bulletin of Engine Building, 2017, Vol. 1, pp. 56–66. [In Russian]

Yefanov V., Prokopenko O., Ovchinnikov O., Vnukov U. Erosion resistance of compressor blades of helicopter gas turbine engines with various types of coatings, Bulletin of Engine Building, 2017, Vol. 1, pp. 120–123. [In Russian]

Dvirnyk Ya., Pavlenko D. Patterns of wear of the compressor blades of helicopter engines operating in a dusty atmosphere, Bulletin of Engine Building, 2016, Vol. 1, pp. 42–51. [In Russian]

Huang H. Statistical Mechanics of Neural Networks, Berlin: Springer, 2022, 314 p.

Ekman M. Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow, Boston, Addison-Wesley Professional, 2021, 752 p.

Aggarwal C. C. Neural Networks and Deep Learning: A Textbook, Berlin, Springer, 2018, 520 p.

Wu L., Cui P., Pei J., Zhao L. Graph Neural Networks: Foundations, Frontiers, and Applications, Berlin, Springer, 2022, 752 p.

Kneusel R.T. Math for Deep Learning: What You Need to Know to Understand Neural Networks, San Francisco, No Starch Press, 2021, 344 p.

Chapmann J. Neural Networks: Introduction to Artificial Neurons, Backpropagation Algorithms and Multilayer Feedforward Networks (Advanced Data Analytics), Scotts Valley, CreateSpace Independent Publishing Platform, 2017, 108 p.

Wadi H. Learn From Scratch Backpropagation Neural Networks using Python GUI & MariaDB, Chicago, Independently published, 2021, 590 p.

Milani A., Carpi A., Poggioni V. Evolutionary Algorithms in Intelligent Systems, Basel: Mdpi AG, 2020, 144 p.

Leoshchenko S., Oliinyk A., Subbotin S., Lytvyn V., Shkarupylo V. Modification and parallelization of genetic algorithm for synthesis of artificial neural networks, Radio Electronics, Computer Science, Control, 2019, No. 4, pp. 68–82. DOI: 10.15588/1607-3274-2019-4-7

Subbotin S., Pukhalska H., Leoshchenko S., Oliinyk A., Gofman Ye. Neuromodeling of operational processes, Radio electronics, computer science, control, 2022, No. 1, pp. 120–129.

Leoshchenko S., Subbotin S., Oliinyk A., Narivs’kiy O. Implementation of the indicator system in modeling complex technical systems, Radio electronics, computer science, control, 2021, No. 1, pp. 117–127. DOI: 10.15588/1607-3274-2021-1-12.

Downloads

Published

2022-06-18

How to Cite

Leoshchenko, S. D., Pukhalska, H. V., Subbotin, S. A., Oliinyk, A. O., & Gofman, Y. O. (2022). NEURAL NETWORK DIAGNOSTICS OF AIRCRAFT PARTS BASED ON THE RESULTS OF OPERATIONAL PROCESSES . Radio Electronics, Computer Science, Control, (2), 69. https://doi.org/10.15588/1607-3274-2022-2-7

Issue

Section

Neuroinformatics and intelligent systems

Most read articles by the same author(s)

> >>