Russian Federation
graduate student
VAC 2.3.6 Методы и системы защиты информации, информационная безопасность
UDK 004.051 Эффективность
The article considers modern approaches to diagnostics and recovery of the rolling stock information management systems (IMS). Introduction: in the context of increasing demands on the transport system reliability and safety, traditional diagnostic methods are becoming ineffective. Purpose: to explore the possibilities of using multi-agent and neural network technologies to improve the IMS diagnostics and recovery efficiency on the rolling stock. Methods: the paper analyzes multi-agent systems that provide distributed diagnostics where agents interact for malfunction detection and localization. Neural network technologies that ensure high accuracy of fault prediction through the analysis of large amounts of data and self-learning are also being investigated. Results: the main result is to identify the potential for integrating multi-agent and neural network technologies. Their combined use can significantly increase the reliability, adaptability and autonomy of the rolling stock IMS. Discussion: the prospects of creating hybrid systems combining multi-agent and neural network methods are discussed. Their advantages and limitations are considered and the potential for further improvement of transport system IMS is illustrated.
multi-agent systems, neural network technologies, transport, information control systems, automation
1. Kon E. L., Kulagina M. M. Nadezhnost i diagnostika komponentov infokommunikatsionnykh i informatsionno-upravlyayushchikh sistem: Uchebnoe posobie. Perm, Perm National Research Polytechnic University, 2012, 395 p. (In Russian)
2. Yazynin V. S., Baranovsky A. M. Model informatsionnoy sistemy kontrolya kharakteristik tokopriemnika na osnove primeneniya 3D-skanirovaniya i neyrosetevykh tekhnologiy, Transport: problemy, idei, perspektivy: sbornik trudov LXXXIII Vserossiyskoy nauchno-tekhnicheskoy konferentsii studentov, aspirantov i molodykh uchenykh, Saint Petersburg, Russia, April 17–24, 2023. Vol. 1. Saint Petersburg, Petersburg State Transport University, 2023. C. 97–102. (In Russian) EDN: https://elibrary.ru/FSZSPP
3. Yazynin V. S., Baranovsky A. M., Zabrodin A. V. The Model of Remote Monitoring System for Electric Rolling Stock Condition Using Artificial Neural Networks, Intellectual Technologies on Transport, 2023, №. 1 (33), Pp. 27–37. DOI:https://doi.org/10.24412/2413-2527-2023-133-27-37. (In Russian) EDN: https://elibrary.ru/GRDYTQ
4. Kharchenko V. S., Elyasi Komari I, Gorbenko A. V. Instrumentation and Control Systems Dependability Assessment Using Hierarchical FME(C)A-Tables and Markov's Chains: Models, Technique and Information Technology, Belgorod State University Scientific Bulletin. History. Political Science. Economics. Information Technologies, 2011, No. 19 (114), Iss. 20/1, Pp. 169–178. (In Russian)
5. Isaev I. D. Multi-Agent Systems, Algorithm for Pattern Recognition by Intelligent Agents, Vestnik Nauki, 2023, No. 11 (68), Pp. 651–659. (In Russian) EDN: https://elibrary.ru/VOQPRA
6. Alibekov B. I. Mamaev E. A. Multi-Agent Systems of Logistics: Information and Analytical Aspects, Herald of Dagestan State University. Series 1. Natural Sciences, 2017, Vol. 32. Iss. 4, Pp. 56-62. DOI:https://doi.org/10.21779/2542-0321-2017-32-4-56-62. (In Russian) EDN: https://elibrary.ru/YLQBGG
7. Gasnikov A. V. (ed.) Vvedenie v matematicheskoe modelirovanie transportnykh potokov: Uchebnoe posobie. Moscow, Moscow Center for Continuous Mathematical Education, 2013, 428 p. (In Russian)
8. Kesting A., Treiber M., Helbing D. Agents for Traffic Simulation. In: Uhrmacher A. M., Weyns D. (eds.) Multi-Agent Systems: Simulation and Applications. Boca Raton (FL), CRC Press, 2009, Pp. 325–356.
9. Bochkov A. P., Baranovskii A. M., Gilvanov R. G. Assessment of Consistency and Compatibility of Technical Systems in Complex Organizational and Technical Systems, Systems of Control, Communication and Security, 2020, No. 1, Pp. 284–301. DOI:https://doi.org/10.24411/2410-9916-2020-10109 (In Russian) EDN: https://elibrary.ru/CIXCWP
10. Vyucheyskaya M. V., Kraynova I. N., Gribanov A. V. Neural Network Technologies in Medical Diagnosis (Review), Journal of Medical and Biological Research, 2018, Vol. 6, No. 3, Pp. 284–294. DOI:https://doi.org/10.17238/issn2542-1298.2018.6.3.284. (In Russian) EDN: https://elibrary.ru/UZPSJS
11. Yazinin V. S., Baranovskiy A. M., Vorobyev A. A., Romanova I. Yu. The Use of Artificial Neural Networks on Russian Railways to Control Current Collectors of Trains, International Journal of Advanced Studies, 2023, Vol. 13, No. 1, Pp. 267–287. DOI:https://doi.org/10.12731/2227-930X-2023-13-1-267-287. (In Russian) EDN: https://elibrary.ru/UGLBCH
12. Khamidulin T. G. Application of Artificial Neural Networks in the Transport Industry, Economics and Society, 2019, No. 4 (59), Pp. 851–858. (In Russian) EDN: https://elibrary.ru/ZZRFOP
13. Gluhov A. P., Baranovskiy A. M., Fomenko Y. S., Bochkov A. P. Adapted Model Neural-Like Hopfield Network and the Algorithm of Its Training for Finding the Roots Systems of Linear Algebraic Equations. In: Silhavy R. (ed.) Artificial Intelligence in Intelligent Systems (CSOC 2021): Proceedings of the 10th Computer Science On-line Conference 2021 (Zlín, Czech Republic, April 2021), Vol. 2. Lecture Notes in Networks and Systems, Vol. 229. Cham, Springer Nature, 2021, Pp. 386–395. DOI:https://doi.org/10.1007/978-3-030-77445-5_36. EDN: https://elibrary.ru/OYIMXW
