DOI: https://doi.org/10.32515/2664-262X.2025.12(43).1.272-288
Increasing the Wear Resistance and Reliability of Resource-Determining Parts of Transport and Agricultural Machinery by Engineering Methods Using Neural Networks
About the Authors
Vitaliy Chumak, PhD student in Industrial Mechanical Engineering, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0009-0002-1913-9371, e-mail: vitaly.chumak33@gmail.com
Viktor Aulin, Professor, Doctor of Technical Sciences, Professor of the Department of Machinery Operation and Repair, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0003-2737-120X, e-mail: aulinvv@gmail.com
Andrii Grynkiv, Senior Researcher, PhD (Candidate of Technical Sciences), Senior Lecturer of the Department of Machinery Operation and Repair, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-4478-1940, e-mail: AVGrinkiv@gmail.com
Serhii Lysenko, Professor, Doctor of Technical Sciences, Professor of the Department of Computer Engineering and Information Systems, Khmelnytskyi National University, Khmelnytskyi, Ukraine, ORCID: 0000-0001-7243-8747, e-mail: lysenkos@khmnu.edu.ua
Oleksandr Kuzyk, Associate Professor, Candidate of Technical Sciences, Head of the Department of Materials Science and Foundry Production, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-3047-3760, e-mail: kuzykov@gmail.com
Abstract
It is shown that in modern conditions of intensive development of Industry 4.0 and digitalization of machine-building processes, the problem of ensuring the reliability and durability of resource-determining parts of transport and agricultural machinery is of particular relevance. It is found that traditional approaches to predicting the technical condition of machines, based on calendar maintenance, do not meet modern requirements of economic efficiency and operational safety.
A methodology is proposed, which is based on the hybrid application of artificial neural networks - a multilayer perceptron (MLP) for identifying the dominant mechanisms of wear of machine parts and their conjugation and a recurrent long short-term memory network (LSTM) for predicting the dynamics of part degradation based on time series of operational parameters. It was determined that the use of synthetic data generated on the basis of physical models of wear allows overcoming the limitations associated with the lack of real operational data of machine components, systems and assemblies.
Validation of the developed algorithm on a representative data set (50,000 samples) demonstrated high prediction accuracy: coefficient of determination R² = 0.98...0.99, root mean square error RMSE = 8.12...15.67 μm, mean absolute percentage error MAPE = 2.5 3.9%. These results confirm the prospects of integrating the
proposed approach into cyber-physical systems of modern transport and agricultural machinery for implementing the concept of predictive maintenance.
Keywords
wear prediction, artificial intelligence, MLP, LSTM, synthetic data, Archard model, predictive maintenance
Full Text:
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References
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Citations
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Copyright (©) 2025, Vitaliy Chumak, Viktor Aulin, Andrii Grynkiv, Serhii Lysenko, Oleksandr Kuzyk
Increasing the Wear Resistance and Reliability of Resource-Determining Parts of Transport and Agricultural Machinery by Engineering Methods Using Neural Networks
About the Authors
Vitaliy Chumak, PhD student in Industrial Mechanical Engineering, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0009-0002-1913-9371, e-mail: vitaly.chumak33@gmail.com
Viktor Aulin, Professor, Doctor of Technical Sciences, Professor of the Department of Machinery Operation and Repair, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0003-2737-120X, e-mail: aulinvv@gmail.com
Andrii Grynkiv, Senior Researcher, PhD (Candidate of Technical Sciences), Senior Lecturer of the Department of Machinery Operation and Repair, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-4478-1940, e-mail: AVGrinkiv@gmail.com
Serhii Lysenko, Professor, Doctor of Technical Sciences, Professor of the Department of Computer Engineering and Information Systems, Khmelnytskyi National University, Khmelnytskyi, Ukraine, ORCID: 0000-0001-7243-8747, e-mail: lysenkos@khmnu.edu.ua
Oleksandr Kuzyk, Associate Professor, Candidate of Technical Sciences, Head of the Department of Materials Science and Foundry Production, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-3047-3760, e-mail: kuzykov@gmail.com
Abstract
Keywords
Full Text:
PDFReferences
1. Aulin, V. V., Hryn'kiv, A. V., & Holovatyi, A. O. (2020). Cyber-physical approach to the creation of transport and production systems. Tsentralnoukrains'kyi naukovyi visnyk. Tekhnichni nauky, 3(34), 331–343 [in Ukrainian].
2. Holovatyi, A. O., Chumak, V. M., Man’ko, Ye. V., et al. (2025). Improvement of mathematical modeling of mechanical engineering technologies for smart enterprises in the machine vision system. Tsentralnoukrains'kyi naukovyi visnyk. Tekhnichni nauky, 11(42), part 2, 143–159. Kropyvnytskyi: CNTU [in Ukrainian]. https://doi.org/10.32515/2664-262X.2025.11(42).2.143-159 (accessed September 1, 2025).
3. Indus, M. P., & Levchenko, O. V. (2019). Mathematical modeling of technical systems. Kyiv: Lybid’ [in Ukrainian].
4. Zhang, B., Zhang, S., & Li, W. (2019). Bearing performance degradation assessment using long short-term memory recurrent network. Computers in Industry, 106, 14–29. doi.org/10.1016/j.compind.2018.12.01 (accessed September 15, 2025).
5. Singh, K., Kumar, S., Singh, K. K., et al. (2022). Computational data-driven based optimization of tribological performance of graphene filled glass fiber reinforced polymer composite using machine learning approach. Materials Today: Proceedings, 66, 3838–3846. https://doi.org/10.1016/j.matpr.2022.06.25 (accessed September 15, 2025).
6. Susto, G. A., Schirru, A., Pampuri, S., et al. (2015). Machine learning for predictive maintenance: A multiple classifier approach. IEEE Transactions on Industrial Informatics, 11(3), 812–820. https://doi.org/10.1109/TII.2014.2349359 (accessed September 2, 2025).
7. Shah, R., Pai, N., Thomas, G., et al. (2025). Machine learning in wear prediction. Journal of Tribology, 147(4), Article 040801. https://doi.org/10.1115/1.4066865 (accessed September 1, 2025).
8. Wang, X., Qin, Q., Dai, S., et al. (2025). Machine learning-based prediction of mechanical properties for large bearing housing castings. Materials, 18(17), 4036. https://doi.org/10.3390/ma18174036 (accessed 18/08/2025).
9. Liu, Y., Pan, D., Zhang, H., et al. (2023). Remaining useful life prediction of bearing via a double attention-based deep neural network. In 2022 IEEE Smart World, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse (pp. 92). Piscataway: IEEE. https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp- Metaverse56740.2022.00092 (accessed September 12, 2025).
10. Asmai, S. A., Basari, A. S. H., Shibghatullah, A. S., et al. (2011). Neural network prognostics model for industrial equipment maintenance. In 2011 11th International Conference on Hybrid Intelligent Systems (HIS) (pp. 637–642). Piscataway: IEEE. https://doi.org/10.1109/HIS.2011.6122176 (accessed August 18, 2025).
11. Paredes, J., Chávez, D., Isa-Jara, R., et al. (2025). A hybrid machine learning algorithm approach to predictive maintenance tasks: A comparison with machine learning algorithms. Results in Engineering, 25, 105137. https://doi.org/10.1016/j.rineng.2025.105137 (accessed August 18, 2025).
12. Kisten, M., Ezugwu, A. E., & Olusanya, M. O. (2024). Explainable artificial intelligence model for predictive maintenance in smart agricultural facilities. IEEE Access, 12, 24348–24367. doi.org/10.1109/ACCESS.2024.3365586 (accessed August 26, 2025).
13. Tran, K. P. (2021). Artificial intelligence for smart manufacturing: Methods and applications. Sensors, 21(16), 5584. https://doi.org/10.3390/s21165584 (accessed August 20, 2025).
14. Mondal, S., & Goswami, S. S. (2024). Machine learning applications in automotive engineering: Enhancing vehicle safety and performance. Journal of Process Management New Technologies, 12(1–2), 61–71. https://doi.org/10.5937/jpmnt12-50607 (accessed August 18, 2025).
15. Jin, L. (2021). Application of neural network method in engineering prediction. Journal of Physics: Conference Series, 2083(4), Article 042080. https://doi.org/10.1088/1742-6596/2083/4/042080 (accessed August 17, 2025).
16. Ali, Y. (2018). Artificial intelligence application in machine condition monitoring and fault diagnosis. In Artificial Intelligence – Emerging Trends and Applications. IntechOpen. doi.org/10.5772/intechopen.74932 (accessed September 2, 2025).
17. Olivares, D. C. (2025). Feature engineering for data-based predictive maintenance (Doctoral dissertation, Universidad de Sevilla) [in Spanish].
18. Baptista, M., de Medeiros, I. P., Malere, J. P., et al. (2016). Comparative case study of life usage and data- driven prognostics techniques using aircraft fault messages. Computers in Industry, 87, 68–81. https://doi.org/10.1016/j.compind.2016.12.008 (accessed September 3, 2025).
19. Ismoilov, N., & Jang, S.-B. (2018). A comparison of regularization techniques in deep neural networks. Symmetry, 10(11), 648. https://doi.org/10.3390/sym10110648 (accessed September 3, 2025).
20. Chien, C.-F., Ku, C.-C., & Lu, Y.-Y. (2023). Ensemble learning for demand forecast of after-market spare parts to empower data-driven value chain and an empirical study. Computers & Industrial Engineering, 184, 109670. https://doi.org/10.1016/j.cie.2023.109670 (accessed September 3, 2025).
21. Dix, M., Manca, G., & Fay, A. (2025). Measuring the robustness of supervised ML models to label noise in industrial data. In 2025 IEEE 8th International Conference on Industrial Cyber-Physical Systems (ICPS), Article 11087835. IEEE. https://doi.org/10.1109/ICPS65515.2025.11087835 (accessed September 3, 2025).
22. Lisiecki, A. (2019). Tribology and surface engineering. Coatings, 9(10), 663. https://doi.org/10.3390/coatings9100663 (accessed August 30, 2025).
23. Hofmann, M., Neukart, F., & Bäck, T. (2017). Artificial intelligence and data science in the automotive industry. arXiv. https://doi.org/10.48550/arXiv.1709.01989 (accessed August 30, 2025).
24. Hermann, M., Pentek, T., & Otto, B. (2016). Design principles for Industrie 4.0 scenarios. In 2016 49th Hawaii International Conference on System Sciences (HICSS) (pp. 3928–3937). IEEE. https://doi.org/10.1109/HICSS.2016.488 (accessed August 30, 2025).
Citations
1. Аулін В. В., Гриньків А. В., Головатий А. О. Кіберфізичний підхід при створенні транспортно-виробничих систем. Центральноукраїнський науковий вісник. Технічні науки. 2020. Вип. 3 (34). С. 331–343.
2. Головатий А. О., Чумак В. М., Манько Є. В. та ін. Вдосконалення математичного моделювання машинобудівних технологій для смарт-підприємств в системі машинного зору. Центральноукраїнський науковий вісник. Технічні науки : зб. наук. пр. Кропивницький : ЦНТУ, 2025. Вип. 11(42), ч. 2. С. 143–159. URL: https://doi.org/10.32515/2664-262X.2025.11(42).2.143-159 (дата звернення: 01.09.2025).
3. Індус М. П., Левченко О. В. Математичне моделювання технічних систем. К. : Либідь, 2019. 312 с.
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