Збірник наукових праць

DOI: https://doi.org/10.32515/2664-262X.2025.12(43).2.204-219

A Universal Method for Formalizing Parameters of Resource-Determining Parts of Transport and Agricultural Machinery for Predictive Maintenance Systems

Vitaliy Chumak, Yehor Manko, Viktor Baitsan, Viktor Aulin, Serhii Lysenko, Andrii Hrynkiv

About the Authors

Vitaliy Chumak, PhD student, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0009-0002-1913-9371, e-mail: vitaly.chumak33@gmail.com

Yehor Manko, PhD student, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0009-0005-6355-2413, e-mail: evm0996496486@gmail.com

Viktor Baitsan, PhD student, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0009-0006-3519-9090, e-mail: v.baitsan@landtech-ukraine.com

Viktor Aulin, Professor, Doctor of Technical Sciences, Professor of the Department of Operation and Repair of Machines, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0003-2737-120X, e-mail: aulinvv@gmail.com

Serhii Lysenko, Associate Professor, Candidate of Technical Sciences, Associate Professor of the Department of ERM, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0003-0845-7817, e-mail: sv07091976@gmail.com

Andrii Hrynkiv, Senior Researcher, PhD in Technics (Candidate of Technics 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, е-mail: AVGrinkiv@gmail.com

Abstract

It is shown that in the era of the fourth industrial revolution and comprehensive digitalization of production complexes, the task of ensuring operational stability and extending the service life of critical components of automotive and agro-industrial equipment is becoming critically important. It is established that classical methods of assessing the performance of equipment, which are based on regulated service terms, do not satisfy the current needs of production profitability and operational safety. The key issue is identified - the lack of a unified system for structuring parts parameters: existing methods (FMEA, CAD/PLM systems, specialized tribological databases) are characterized by high specificity and do not allow scaling predictive models to arbitrary types of components without significant experimental costs. Methods for universal formalization of parameters of resource-determining parts in the form of a universal description of parts (UDP), which combines material science, geometric, operational and reliability parameters into a single structured form, are proposed. It is shown that the UDP integrates an explicit specification of operating conditions, material state and wear measurement methods. An expert system has been developed that automatically classifies the dominant wear mechanism according to the UDP parameters and selects the appropriate calibrated physical models: Archard models for abrasive wear, Palmgren-Miner model for fatigue wear and a customized model for wear in soil. A comparative study of UDP with alternative approaches (FMEA, CAD/PLM, tribological DB, IoT) has been conducted on twelve categories of parts of transport (PT) and agricultural machinery (AGM). It has been found that UDP significantly reduces the time for developing predictive models and increases the accuracy of the residual resource forecast: MAPE UDP – 3.2%; FMEA – 16.8% for stable operating conditions. At the same time, universality of application without modification of the structure and scalability of adaptation to new types of parts are ensured. Validation on real operational data of 50 units of equipment for each type of part for 1000...5000 hours confirmed the reliability of the method: the average absolute percentage error MAPE was 3.2...4.4% for transport parts of PT and 4.4 5.2% for AGM parts with variations in operating conditions when using calibrated models in typical conditions. The results confirm the possibility of integrating UDP into predictive maintenance systems and CAD/PLM platforms to provide scalable solutions for determining the remaining service life of PT and AGM parts.

Keywords

universal part description, predictive maintenance, parameter formalization, comparative analysis of methods, wear mechanisms, residual life prediction, physical wear models, expert system, calibrated models

Full Text:

PDF

References

1. Mobley, R. K. (2002). An introduction to predictive maintenance (2nd ed.). Butterworth-Heinemann. https://doi.org/10.1016/B978-075067531-4/50002-8.

2. Lee, J., Kao, H.-A., & Yang, S. (2014). Service innovation and smart analytics for Industry 4.0 and Big Data Environment. Procedia CIRP, 16, 3–8. https://doi.org/10.1016/j.procir.2014.02.001.

3. Aulín, V. V., Hrynkiv, A. V., & Holovatyi, A. O. (2020). Cyber-physical approach in the creation of transport and production systems. Central Ukrainian Scientific Bulletin. Technical Sciences, 3(34), 331–343. https://doi.org/10.32515/2664-262X.2020.3(34).331-343 [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.2018.12.016.

5. ISO 6336-1:2006. (2006). Calculation of load capacity of spur and helical gears. Part 1: Basic principles, introduction and general influence factors (2nd ed.). International Organization for Standardization. https://www.iso.org/standard/36327.html.

6. Archard, J. F. (1953). Contact and rubbing of flat surfaces. Journal of Applied Physics, 24(8), 981–988. https://doi.org/10.1063/1.1721448.

7. ISO 281:2007. (2007). Rolling bearings – Dynamic load ratings and rating life (2nd ed.). International Organization for Standardization. https://www.iso.org/standard/38102.html.

8. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.

9. Carvalho, T. P., Soares, F. A. A. M., Vita, R., Francisco, R. P., Basto, J. P., & Alcalá, S. G. S. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024. https://doi.org/10.1016/j.cie.2019.106024.

10. Tran, K. P. (2021). Artificial intelligence for smart manufacturing: Methods and applications. Sensors, 21(16), 5584. https://doi.org/10.3390/s21165584.

11. Chumak, V. M., Aulin, V. V., Hrynkiv, A. V., Lysenko, S. V., & Kuzyk, O. V. (2025). Improvement of wear resistance and reliability of resource-determining parts of transport and agricultural machinery by methods of engineering optimization. Central Ukrainian Scientific Bulletin. Technical Sciences, 11(42, part 2), 143–159. https://doi.org/10.32515/2664-262X.2025.12(43).1.272-288 [in Ukrainian].

12. Stark, J. (2015). Product lifecycle management (Volume 1): 21st century paradigm for product realisation (4th ed.). Springer. https://doi.org/10.1007/978-3-030-98578-3.

13. Carlson, C. S. (2012). Effective FMEAs: Achieving safe, reliable, and economical products and processes using failure mode and effects analysis. John Wiley & Sons, Inc. https://doi.org/10.1002/9781118312575.

14. Bhushan, B. (2000). Modern tribology handbook (1st ed.). CRC Press. doi.org/10.1201/9780849377877.

15. Meng, H. C., & Ludema, K. C. (1995). Wear models and predictive equations: Their form and content. Wear, 181–183, 443–457. https://doi.org/10.1016/0043-1648(95)90158-2.

16. Hutchings, I., & Shipway, P. (2017). Tribology: Friction and wear of engineering materials (2nd ed.). Butterworth-Heinemann. https://www.sciencedirect.com/book/9780081009109/tribology.

17. Shao, S., McAleer, S., Yan, R., & Baldi, P. (2019). Highly accurate machine fault diagnosis using deep transfer learning. IEEE Transactions on Industrial Informatics, 15(4), 2446–2455. https://doi.org/10.1109/TII.2018.2864759.

18. Zum Gahr, K. H. (1987). Microstructure and wear of materials. Elsevier. https://www.academia.edu/82805296/Karl_Heinz_Zum_Gahr_Microstructure_and_Wear_of_Materials.

19. Natsis, A., Papadakis, G., & Pitsilis, J. (1999). The influence of soil type, soil water and share sharpness of a mouldboard plow on energy consumption, speed of operation and ploughing quality. Journal of Agricultural Engineering Research, 72(2), 171–176. https://doi.org/10.1006/jaer.1998.0360.

20. Horvat, Z., Filipović, D., Kosutic, S., & Emert, R. (2008). Reduction of mouldboard plough share wear by a combination technique of hardfacing. Tribology International, 41(8), 778–782. doi.org/10.1016/j.triboint.2008.01.008.

21. Zheng, J., Li, W., & Li, J. (2024). A comparative study on the wear behavior of quenched-and-partitioned steel (Q&P) and martensite steel (Q&T). Coatings, 14(6), 727. https://doi.org/10.3390/coatings14060727

22. Bayhan, Y. (2006). Reduction of wear via hardfacing of chisel ploughshare. Tribology International, 39(6), 570–574. https://doi.org/10.1016/j.triboint.2005.06.005.

23. Davis, J. R. (Ed.). (2001). Alloying: Understanding the basics. ASM International. doi.org/10.31399/asm.tb.aub.9781627082976.

24. Hutchings, I., & Shipway, P. (2017). Tribology: Friction and wear of engineering materials (2nd ed.). Butterworth-Heinemann. https://www.sciencedirect.com/book/9780081009109/tribology.

25. Williams, J. A. (2005). Engineering tribology. Cambridge University Press. doi.org/10.1017/CBO9780511805905.

26. Tyshchenko, S. Yu., Serhiichuk, A. A., Aulin, V. V., & Hrynʹkiv, A. V. (2025). Methods for determining the needs of car service enterprises for spare parts and developing a methodology for their impact on the overall performance of vehicles. Central Ukrainian Scientific Bulletin. Technical Sciences: Collection of Scientific Papers, 11(42), Part 1, 243–257. https://doi.org/10.32515/2664-262X.2025.11(42).1.243-257 [in Ukrainian].

27. Aulin, V. V., Hrynʹkiv, A. V., Lysenko, S. V., et al. (2020). Principles of construction and functioning of a cyber- physical system for technical service of automotive and mobile agricultural machinery. Technical Service of the Agro-Industrial, Forestry and Transport Complexes: Scientific Journal, (22), 162–174. https://doi.org/10.37700/ts.2020.22.162-174 [in Ukrainian].

28. Serhiichuk, A. A., Hrynʹkiv, A. V., & Aulin, V. V. (2025). Development of an innovative quality management system for vehicle maintenance and repair based on the process approach. In Kramarov Readings: Proceedings of the 12th International Scientific and Technical Conference (Kyiv, February 20–21, 2025) (pp. 145–147). Kyiv: Publishing Center of NUBiP of Ukraine. [in Ukrainian].

29. Hrynʹkiv, A. V., & Aulin, V. V. (2022). Element-modular system of maintenance and repair of mobile agricultural machinery. In Processes, Machines and Equipment of Agro-Industrial Production: Problems of Theory and Practice: Proceedings of the International Scientific and Practical Conference (Ternopil, September 29–30, 2022) (pp. 103–104). Ternopil: FOP Palianytsia V. A. [in Ukrainian].

Citations

1. Mobley, R. Keith. An introduction to predictive maintenance. R. Keith Mobley. 2nd ed. Oxford : Butterworth- Heinemann, 2002. 532 p.

2. Lee, J., Kao, H.-A., Yang, S. Service innovation and smart analytics for Industry 4.0 and Big Data environment. Procedia CIRP. 2014. Vol. 16. P. 3–8. URL: https://doi.org/10.1016/j.procir.2014.02.001

3. Аулін В. В., Костенко С. І., Мартиненко О. О., Баранов В. Л. Кіберфізичний підхід у створенні транспортних та виробничих систем. Центральноукраїнський науковий вісник. Технічні науки. 2020. Т. 3, №34. С. 331–343. URL: https://doi.org/10.32515/2664-262X.2020.3(34).331-343

4. Zhang, B., Zhang, L., Li, W., Gao, L., Zhang, Z. Bearing performance degradation assessment using long short- term memory recurrent network. Computers in Industry. 2019. Vol. 106. P. 14–29. URL: https://doi.org/10.1016/j.compind.2018.12.016

5. ISO 6336-1:2006. Calculation of load capacity of spur and helical gears. Part 1: Basic principles, introduction and general influence factors. 2nd ed. Geneva : International Organization for Standardization, 2006. 109 p. URL: https://www.iso.org/standard/36327.html

6. Archard, J. F. Contact and rubbing of flat surfaces. Journal of Applied Physics. 1953. Vol. 24, No. 8. P. 981–988. URL: https://doi.org/10.1063/1.1721448

7. ISO 281:2007. Rolling bearings: dynamic load ratings and rating life. 2nd ed. Geneva : International Organization for Standardization, 2007. 51 p. URL: https://www.iso.org/standard/38102.html

8. Hochreiter, S., Schmidhuber, J. Long short-term memory. Neural Computation. 1997. Vol. 9, No. 8. P. 1735– 1780. URL: https://doi.org/10.1162/neco.1997.9.8.1735

9. Carvalho, T. P., Soares, F. A. A. M. N., Vita, R., Francisco, R. de, Basto, J. P., Alcalá, S. G. S. A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering. 2019. Vol. 137. P. 106024. URL: https://doi.org/10.1016/j.cie.2019.106024

10. Tran, K. P. Artificial intelligence for smart manufacturing: methods and applications. Sensors. 2021. Vol. 21, No.

16. P. 5584. URL: https://doi.org/10.3390/s21165584

11. Чумак В. М., Аулін В. В., Мартиненко О. О., Костенко С. І. Підвищення зносостійкості та надійності ресурсовизначальних деталей транспортної та сільськогосподарської техніки методами інженерної оптимізації. Центральноукраїнський науковий вісник. Технічні науки. 2025. Т. 11, № 42, ч. 2. С. 143–159. URL: https://doi.org/10.32515/2664-262X.2025.12(43).1.272-288

12. Stark, J. Product lifecycle management (Volume 1): 21st century paradigm for product realisation. 4th ed. Cham : Springer, 2015. 406 p. (Decision Engineering). URL: https://doi.org/10.1007/978-3-030-98578-3

13. Carlson, C. S. Effective FMEAs: achieving safe, reliable, and economical products and processes using failure mode and effects analysis. Hoboken : John Wiley & Sons, Inc., 2012. 480 p. URL: https://doi.org/10.1002/9781118312575

14. Bhushan, B. Modern tribology handbook. 1st ed. Boca Raton : CRC Press, 2000. 1720 p. URL: https://doi.org/10.1201/9780849377877

15. Meng, H. C., Ludema, K. C. Wear models and predictive equations: their form and content. Wear. 1995. Vol. 181–183. P. 443–457. URL: https://doi.org/10.1016/0043-1648(95)90158-2

16. Hutchings, I., Shipway, P. Tribology: Friction and Wear of Engineering Materials. 2nd ed. Oxford : Butterworth- Heinemann, 2017. 412 p. URL: https://www.sciencedirect.com/book/9780081009109/tribology

17. Shao, S., et al. Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning. IEEE Transactions on Industrial Informatics. 2019. Vol. 15, No. 4. P. 2446–2455. URL: doi.org/10.1109/TII.2018.2864759

18. Zum Gahr, K. H. Microstructure and Wear of Materials. Amsterdam : Elsevier, 1987. 560 p. URL: https://www.academia.edu/82805296/Karl_Heinz_Zum_Gahr_Microstructure_and_Wear_of_Materials

19. Natsis A., Papadakis G., Pitsilis J., Karamoutis C. The influence of soil type, soil water and share sharpness of a mouldboard plow on energy consumption, speed of operation and ploughing quality. Journal of Agricultural Engineering Research. 1999. Vol. 72, No. 2. P. 171–176. URL: doi.org/10.1006/jaer.1998.0360

20. Horvat Z., Filipovic D., Kosutic S., Emert R. Reduction of mouldboard plough share wear by a combination technique of hardfacing. Tribology International. 2008. Vol. 41, No. 8. P. 778–782. URL: https://doi.org/10.1016/j.triboint.2008.01.008

21. Zheng J., Li W., Li J. A comparative study on the wear behavior of quenched-and-partitioned steel (Q&P) and martensite steel (Q&T). Coatings. 2024. Vol. 14, No. 6. P. 727. URL: https://doi.org/10.3390/coatings14060727

22. Bayhan Y. Reduction of wear via hardfacing of chisel ploughshare. Tribology International. 2006. Vol. 39, No. 6.P. 570–574. URL: https://doi.org/10.1016/j.triboint.2005.06.005

23. Davis J. R. (ed.) Alloying: Understanding the Basics. Materials Park: ASM International, 2001. 647 p. URL: https://doi.org/10.31399/asm.tb.aub.9781627082976

24. Hutchings I., Shipway P. Tribology: Friction and Wear of Engineering Materials. 2nd ed. Oxford: Butterworth- Heinemann, 2017. 412 p. URL: https://www.sciencedirect.com/book/9780081009109/tribology

25. Williams J. A. Engineering Tribology. Cambridge: Cambridge University Press, 2005. 511 p. URL: https://doi.org/10.1017/CBO9780511805905

26. Тищенко С. Ю., Сергійчук А. А., Аулін В. В., Гриньків А. В. Методи визначення потреб підприємств автосервісу в запасних частинах та розробка методики їх впливу на загальну працездатність автомобілів. Центральноукраїнський науковий вісник. Технічні науки: збірник наукових праць. Кропивницький: ЦНТУ, 2025. Вип. 11(42), ч. 1. С. 243–257.

27. Аулін В. В., Гриньків А. В., Лисенко С. В. [та ін.] Принципи побудови та функціонування кіберфізичної системи технічного сервісу автотранспортної та мобільної сільськогосподарської техніки. Технічний сервіс агропромислового, лісового та транспортного комплексів: науковий журнал. Харків: ХНТУСГ, 2020. №22. С. 162-174. DOI: https://doi.org/10.37700/ts.2020.22.162-174

28. Сергійчук А. А., Гриньків А. В., Аулін В. В. Розробка інноваційної системи управління якістю технічного обслуговування і ремонту транспортних засобів на основі процесного підходу. Крамаровські читання: збірник тез доповідей ХІІ Міжнародної науково-технічної конференції (м. Київ, 20–21 лютого 2025 р.). Київ: Видавничий центр НУБіП України, 2025. С. 145–147.

29. Гриньків А. В., Аулін В. В. Елементно-модульна система технічного обслуговування і ремонту мобільної сільськогосподарської техніки. Процеси, машини та обладнання агропромислового виробництва: проблеми теорії та практики: збірник тез доповідей міжнародної науково-практичної конференції (м. Тернопіль, 29-30 вересня 2022 р.). Тернопіль: ФОП Паляниця В. А., 2022. С. 103–104.