Журнал «Информационные технологии и вычислительные системы» - Н. Н. Бахтадзе, А. Е. Коньков, Д. В. Елпашев, В. Н. Кушнарев, К. С. Мухтаров, А. В. Пуртов, В. Е. Пятецкий, А. А. Черешко "Методы синтеза цифровых двойников на основе цифровых идентификационных моделей производственных процессов"

Представлен подход к созданию цифровых двойников нового типа. Предлагается использовать идентификаторы в цепи обратной связи систем управления, формирующие точечные идентификационные модели на основе ассоциативных знаний. Описаны методы формирования управляющих воздействий в условиях возможной резкой смены режима функционирования управляемого процесса.

цифровой двойник, система управления с идентификатором, цифровые алгоритмы прогнозирования, ассоциативный поиск, индуктивные знания.

1. Forbes, M.G., Patwardhan, R.S., Hamadah, H., and Gopaluni, B.R. (2015). Model Predictive Control in Industry: Challenges and Opportunities. IFAC-PapersOnLine 48–8: 531 – 538.

2. Цифровые двойники в высокотехнологичной промышленности: монография / под ред. А. И. Боровкова. – СПб.: Политех-Пресс, 2022. – 492 с.

3. Negri E., Fumagalli L., Macchi M. A Review of the Roles of Digital Twin in CPS based Production Systems // Procedia Manufacturing, 2017, vol. 11, pp. 939–948

4. В.М. Дозорцев. Цифровые двойники в промышленности: генезис, состав, терминология, технологии, платформы, перспективы. Часть 1. возникновение и становление цифровых двойников как существующие определения отражают содержание и функции цифровых двойников // Автоматизация в промышленности. 2020. № 9. С. 3-11.

5. Дозорцев В.М., Ицкович Э.Л., Кнеллер Д.В. Усовершенствованное управление технологическими процессами (АРС): 10 лет в России // Автоматизация в промышленности. 2013. № 1. С. 12-19.

6. Heng A. et al. Rotating machinery prognostics: state of theart, challenges and opportunities // Mechanical Systems and Signal Processing (MSSP), 2009. Vol. 23. pp. 724-739.

7. N. Bakhtadze, A. Chereshko, D. Elpashev, A. Suleykin, A. Purtov. Predictive associative models of processes and situations // IFACPapersOnLine, 2022. Vol. 55, No. 2, pp. 19–24. 14th IFAC Workshop on Intelligent Manufacturing Systems IMS 2022. Tel-Aviv, Israel, 28-30 March 2022.

8. N. Bakhtadze, E. Sakrutina. Е.А. Information Identification Models in Variable Structure Control Systems // International Journal of Control Systems and Robotics. 2016. Vol. 1. pp. 37-43.

9. Wan Sieng Yeo, Agus Saptoro, Perumal Kumar, Manabu Kano. Just-in-time based soft sensors for process industries: A status report and recommendations // Journal of Process Control. Vol. 128, #8 2023, 103025. DOI https://doi.org/10.1016/j.jprocont.2023.103025

10. Stark R. Innovations in digital modelling for next generation manufacturing system design / R. Stark, S. Kind, S. Neumeyer // CIRP Annals. – 2017. – Vol. 66. – pp. 169–172.

12. Ramon López De Mántaras, David Mcsherry, Derek Bridge, David Leake, Barry Smyth, Susan Craw, Boi Faltings, Mary Lou Maher, Michael T. Cox, Kenneth Forbus, Mark Keane, Agnar Aamodt and Ian Watson 2005. Retrieval, reuse, revision, and retention in casebased reasoning. The Knowledge Engineering Review Vol. 20:3, 215–240. DOI: 10.1017/S0269888906000646

13. Ali Louati, Sabeur Elkosantini, Saber Darmoul, LamjedBen Said 2016 A Case-Based Reasoning System to Control Traffic at Signalized Intersections. IFAC-PapersOnLine 49-5, pp. 149–154.

14. Rodrigo G. C. Rocha, Ryan R. Azevedo, Ygor Cesar Sousa, Eduardo de A. Tavares, Silvio Meira 2014 A case-based reasoning system to support the global software development 18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems - KES2014. Procedia Computer Science 2014. №35. pp. 194 – 202.

15. T. Olsson, P. Funk 2012. Case-based reasoning combined with statistics for diagnostics and prognosis 25th International Congress on Condition Monitoring and Diagnostic Engineering IOP Publishing. Journal of Physics: Conference Series 364 012061 DOI:10.1088/1742-6596/364/1/012061

16. L.E.Mujica, J. Vehı, J. Rodellar, P. Kolakowski 2005 A hybrid approach of knowledge-based reasoning for structural assessment Institute of physics publishing smart materials and structures, 14, pp.1554–1562, DOI:10.1088/0964-1726/14/6/048.

17. Y. Li, S.C.K. Shiu, S.K. Pal, J.N.K. Liu. 2006 A rough setbased case-based reasoner for text categorization International Journal of Approximate Reasoning, 41, pp. 229–255.

18. Gómez-Vallejo H.J., Uriel-Latorre B., Sande-Meijide M., Villamarín-Bello B., Pavón R., Fdez-Riverola F., GlezPeña D. 2016 Case-based reasoning system for aiding detection and classification of nosocomial infections Decision Support Systems Vol. 84 pp. 104-116.

19. Douali, N.a , De Roo, J.b, Jaulent, M.-C.a 2012 Clinical Diagnosis Support System based on Case Based Fuzzy Cognitive Maps and Semantic Web 24th Medical Informatics in Europe Conference, MIE 2012; Pisa; Italy; 26 August 2012

20. López, B., Pous, C., Gay, P., Pla, A., Sanz, J., Brunet, J. 2011 EXiT CBR: A framework for case-based medical diagnosis 7development and experimentation Artificial Intelligence in Medicine Volume 51, Issue 2, pp. 81-91.

21. Sreeparna Banerjee, Amrita Roy Chowdhury 2015 Case Based Reasoning in the Detection of Retinal Abnormalities using Decision Trees Procedia Computer Science 46, pp. 402 – 408.

22. Mohamed M. Marzouk, Rasha M. Ahmed. 2011 A case-based reasoning approach for estimating the costs of pump station projects Journal of Advanced Research 2, pp. 289–295.

23. Naderpajouh, N. and Afshar, A. 2008 A case-based reasoning approach to application of value engineering methodology in the construction industry Construction Management and Economics № 26(4). Pp. 363–372.

24. Bakhtadze, N., Kulba, V., Lototsky, V., Maximov, E. Identification Methods Based on Associative Search Procedure. Control Cybernetics 2011, 2, 6–18.

25. Bakhtadze N.; Suleykin A. Industrial digital ecosystems: Predictive models and architecture development issues. Annual Reviews in Control, 2020, pp. 56-64.

26. Vapnik V. N. Estimation of Dependences Based on Empirical Data; Springer-Verlag: New York, US 1982. https://link.springer.com/book/10.1007/0-387-34239-7

27. Bakhtadze, N.; Sakrutina, E.; Jarko, E. Predictive Associative Search Models in Variable Structure Control Systems. WSEAS Transactions on Mathematics 2016, 15, 407-419, https://wseas.com/journals/mathematics/2016/a765806-093.pdf

28. Bakhtadze, N.; Chereshko, A.; Elpashev, D.; Suleykin, A.; Purtov, A. Predictive associative models of processes and situations. IFAC-PapersOnLine, 2022, 55(2), 19-24, https://doi.org/10.1016/j.ifacol.2022.04.163

29. Natalia Bakhtadze and Igor Yadikin. Analysis and Prediction of Electric Power System’s Stability Based on Virtual State Estimators. / Mathematics 2021, 9, 3194, https://doi.org/10.3390/math9243194 .

30. N. Bakhtadze, A. Chereshko, D. Elpashev, I. Yadykin, R. Sabitov, G. Smirnova. Associative Model Predictive Control // IFAC-PapersOnLine · Volume 56, Issue 2, IFAC WC, Yokohama, 2023, Pages 7330-7334. Elsevier, https://doi.org/10.1016/j.ifacol.2023.10.346, https://www.ipu.ru/node/75816

31. N. Bakhtadze and V. Lototsky. Knowledge-Based Models of Nonlinear Systems Based on Inductive Learning / New Frontiers in Information and Production Systems Modeling and Analysis: Incentive Mechanisms, Competence Management, Knowledge-based Production. Heidelberg, Germany: Springer, 2016. pp. 85-104.

32. Moore, E. On the reciprocal of the general algebraic matrix. Bulletin of the American Mathematical Society: New York, US ,1920; Volume 26, pp. 394–395.

33. Penrose, R. A generalized inverse for matrices. Mathematical proceedings of the Cambridge Philosophical Society:Cambridge, Great Britain, 1955; 51, pp. 406–413.

34. N. Bakhtadze and V. Lototsky. Associative Search and Wavelet Analysis Techniques in System Identification // IFAC-PapersOnLine. 2012. Vol. 16, No. 1. pp. 1227-1232, http://www.ifac-papersonline.net/Detailed/54839.html.

35. Samotylova S.A., Torgashov A.Y. Developing a soft sensor for MTBE process based on a small sample // Automation and Remote Control. 2020. V. 81. No 11. P. 2132-2142.

36. Bonett D.G., Wright T.A. Sample Size Requirements for Estimating Pearson, Kendall and Spearman Correlations // Psychometrika. 2000. Vol. 65 (1). Р. 23–28.