Development of new systems for assessing the technical condition of vehicles based on intelligent diagnostics
DOI:
https://doi.org/10.33216/1998-7927-2025-296-10-94-103Keywords:
transport, vehicles, railway transport, railway rolling stock, wagons, technical condition assessment systems, modeling, intelligent diagnosticsAbstract
It has been established that a necessary condition for maintaining the competitiveness and sustainable development of the railway industry is the introduction of innovative technologies, such as intelligent diagnostics. The work has developed a conceptual model of an intelligent diagnostic system that integrates data from diverse sources, such as on-board sensors, external monitoring systems and repair databases. This allows for the formation of a comprehensive and comprehensive picture of the technical condition of the wagon at any time. The developed mathematical models and algorithms based on machine learning, in particular deep neural networks, have demonstrated high accuracy in detecting hidden defects and predicting their development. These algorithms are able to independently learn on large data sets, adapt to new conditions and identify anomalies that cannot be detected using traditional methods. The practical value of the results lies in the creation of a software prototype that implements the functions of intelligent diagnostics, which can be implemented at railway enterprises. This software provides automated monitoring, data visualization and forecasting of maintenance needs. The practical implementation of the developed solutions in the form of a software prototype demonstrates the possibility of automating diagnostic processes and transitioning to predictive maintenance. The implementation of such systems will allow optimizing repair schedules, significantly reducing operating costs and increasing the efficiency of rolling stock use. The results obtained can be used for the further development of monitoring and control systems in railway transport. This research is an important step towards creating "smart" railways, where safety and efficiency are ensured by integrating advanced information technologies. The results can serve as the basis for developing industry standards and recommendations for the application of intelligent diagnostic systems in railway transport in Ukraine. The proposed intelligent diagnostic system allows for automating the assessment of the technical condition of railway cars, reducing costs and increasing safety.
References
1. Implementing intelligent monitoring of the technical condition of locomotive hydraulic transmissions / B. Bondar, O. Ockasov, V. Petrenko, M. Martishevskij. TRANSBALTICA XIII: Transportation Science and Technology, 22 February 2023. Springer, 2023. P. 726–736. DOI: https://doi.org/10.1007/978-3-031-25863-3_70
2. Efimushkin N., Efimushkina N., Orlov S. Intelligent system for railway joint diagnostics. Society 5.0. Studies in Systems, Decision and Control. Springer, 2023. Vol. 437. P. 199–211. DOI: https://doi.org/10.1007/978-3-031-35875-3_16
3. Liabakh N. A., Ignatieva O. V., Shapovalov V. V. Intelligent maintenance and repair on railway transport. Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23). Springer, 2023. Vol. 777. P. 247–259. DOI: https://doi.org/10.1007/978-3-031-43792-2_24
4. Muradian L. A., Pitsenko I. V., Shaposhnyk V. Y. Mathematical model of risks in railway transport during diagnostics of axle boxes of freight cars. Science and Transport Progress. 2021. № 1(97). Р. 94–103. DOI: https://doi.org/10.15802/ stp2022/265424
5. Freight wagon digitalization for condition monitoring and advanced operation / I. Moya, A. Perez, P. Zabalegui et al. Sensors. 2023. № 23(17). 7448. DOI: https://doi.org/10.3390/s23177448
6. Bondarenko I., Lukoševičius V., Neduzha L. Novel ‘closed’ system approach for monitoring the technical condition of railway tracks. Sustainability. 2024. № 16(8). 3180. DOI: https://doi.org/10.3390/su16083180
7. Continuous monitoring of rail vehicle dynamics by means of acceleration measurements / F. Bruni, F. Castelli Dezza, F. Zanelli et al. Proceedings of the International Conference on Railway Condition Monitoring. 2022. DOI: https://doi.org/10.1109/RailCM.2022.00034
8. Determining rational parameters of the capacitive energy storage system for the underground railway rolling stock / O. Fomin, А. Sulym, I. Kulbovsky, P. Khozia, V. Ishchenko. Eastern-European journal of enterprise technologies. 2018. № 2/1(92). Р. 63-71. DOI: 10.15587/1729-4061.2018.126080
9. Theoretical and practical determination of parameters of on-board capacitive energy storage of the underground rolling stock / A.O. Sulim, O.V. Fomin, P.O. Khozya, A. Mastepan. Scientific Bulletin of National Mining University. 2018. Issue 5 (1). P.79-87. DOI: 10.29202/nvngu/2018-5/8
10. Determining the dynamic loading on a semi-wagon when fixing it with a viscous coupling to a ferry deck / O. Fomin, A. Lovska, I. Kulbovskyi, H. Holub, I. Kozarchuk, V. Kharuta. Восточно-Европейский журнал передовых технологий. 2019. № 2(7). С. 6-12. URL: http://nbuv.gov.ua/UJRN/Vejpte_2019_2(7)__2
11. Durability Determination of the Bearing Structure of an Open Freight Wagon Body Made of Round Pipes during its Transportation on the Railway Ferry / O. Fomin, J. Gerlici, A. Lovska, K. Kravchenko, P. Prokopenko, A. Fomina, V. Hauser. Communications-Scientific letters of the University of Zilina. 2019. Vol. 21. № 1. Р. 28-34. URL: https://dspace.snu.edu.ua/handle/123456789/636
12. Improvement of the Model of Power Losses in the Pulsed Current Traction Motor in an Electric Locomotive / S. Goolak, S. Sapronova, V. Tkachenko, Ie. Riabov, Ye. Batrak. Eastern-European Journal of Enterprise Technologies. 2020. Vol. 6. № 5 (108). Р. 38–46. DOI: https://doi.org/10.15587/1729-4061.2020.218542.
13. Method of spectral analysis of traction current of AC electric locomotives / S. Goolak, V. Tkachenko, G. Bureika, G. Vaičiūnas. Transport. 2020. 35(6). Р.658-668. DOI: http://doi.org/10.3846/ transport.2020.14242
14. Refined Model of Asynchronous Traction Electric Motor of Electric Locomotive / S. Goolak, B. Liubarskyi, S. Sapronova, V. Tkachenko, Ie. Riabov. Transport Means - Proceedings of the International Conference, 2021-October. 2021. Р. 455–460.
15. Na K.-M., Lee K., Kim H. Condition Monitoring of Railway Pantograph Using R CNN and Image Processing. Journal of Electrical Engineering & Technology. 2023. Vol. 18. Р. 2407–2416. DOI: https://doi.org/10.1007/s42835-022-01229-6
16. Sun Q., Chen C., Liu X. Condition Monitoring of Railway Vehicle Suspension System Based on PCA SVM Method. In Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic (TEPEN 2024). Springer, 2024. P. 254–261. DOI: https://doi.org/10.1007/978-3-031-70235-8_23
17. Detecting train driveshaft damages using accelerometer signals and differential convolutional neural networks / A. López Galdo, A. Guerrero López, P. M. Olmos, M. J. Gómez García. ArXiv. 2022. DOI: https://doi.org/10.48550/ arXiv.2211.09011
18. Barkhordari P., Galeazzi R., Blanke M. Monitoring of railpad long term condition in turnouts using extreme value distributions. ArXiv. 2021. DOI: https://doi.org/10.48550/arXiv.2101.02567
