AN ALGORITHM FOR INTELLECTUALIZING AUTOMATED CONTROL SYSTEM FAILURE PREVENTION BASED ON PREDICTIVE ANALYTICS

A. A. Lichman, O. Yu. Cherednikova

About the authors

A. A. Lichman, O. Yu. Cherednikova

Личман А.А. – аспирант, ассистент, ФГБОУ ВО ДонНТУ, кафедра компьютерной инженерии, 283001, Донецк, ул. Артема, 58. Область научных интересов: алгоритмы интеллектуализации и автоматизации, компьютерное моделирование, нейронные сети

Чередникова О.Ю. – к.т.н. доцент, ФГБОУ ВО ДонНТУ, кафедра компьютерной инженерии, 283001, Донецк, ул. Артема, 58, Область научных интересов: операционные сети, системное программирование, компьютерное моделирование, нейронные сети

Download PDF

UDC: 004.032.26
DOI: 10.24412/2413-7383- 2025-3-38-148-156
Language: Russian

Abstract: The introduction of automated control systems has affected almost all key sectors of the economy. From the end of the last century to the present day, the environment in which production facilities exist is constantly changing, and automated control systems are forced to change with it. For example, in the last ten years, partial intellectualization of production facilities has made it possible to optimize costs. Predictive analytics is one of the most effective optimization tools. In this article, an intellectualization algorithm was implemented using the statistical ARIMA method and the random forest machine learning algorithm. The results of the algorithm implementation are analyzed.

Keywords: ARIMA, automated control system, intellectualization, time series, optimization.

References

1. Pikalyov YA.S. Obzor arhitektur sistem intellektual'noj obrabotki estestvenno-yazykovyh tekstov // Problem iskusstvennogo intellekta. 2020. № 4 (19). S. 45-68.
2. Burlaeva E.I. Zori S.A. Sravnenie nekotoryh metodov mashinnogo obucheniya dlya analiza tekstovyh dokumentov // Problem iskusstvennogo intellekta. 2019. No. 1 (12). S. 42-51.
3. Kovalchuk Yu. N. Forecasting time series using combined ARIMA and machine learning models / Kovalchuk Yu. N. Pavlova N. A. // Mathematical modeling. - 2021. – Vol. 33, No. 4. – pp. 56-67.
4. Antsyferov S.S. Sigov A.S. Tekhnologicheskie osnovy postroeniya intellektual'nyh sistem // Problem iskusstvennogo intellekta. 2016. № 1 (2). S. 34-44. Алгоритм интеллектуализации предупреждения сбоев АСУ… 4Л
5. Bobrovsky A. Yu. Forecasting time series using the ARIMA model / Bobrovsky A. Yu. – M.: Scientific world, 2015. – 210 p.
6. Gusev V. V. Machine learning for data analysis: from linear models to neural networks / Gusev V. V. – St. Petersburg: BHV-Petersburg, 2018. 256 p.
7. Gavrilov I. V. Machine learning methods in time series forecasting / Gavrilov I. V. // Journal of Computational Mathematics and Mathematical Physics. 2019. Vol. 59, No. 6. pp. 877-886.
8. Ivanov D. M. Introduction to ARIMA models for time series analysis / Ivanov D. M. – M.: "Financial World", 2017. 190 p
9. Kulikov A.V. Algorithms and optimization methods for big data analysis / Kulikov A.V. – St. Petersburg: Nauka Publ. 2019, 180 p.
10. Yakovlev I. I. Methods of analysis and prediction of time series using ARIMA and neural networks / Yakovlev I. I. – St. Petersburg: Business Press, 2018. – 208 p.
11. Agafonova M.S. Development of the adaptation management mechanism of an industrial enterprise. // Successes of Modern Natural Science, 2011. pp. 71-72.
12. Chuvikov, D.A. Models and algorithms for reconstruction and examination of emergency events of road accidents: dis.... Candidate of Technical Sciences: 05.13.01 / M. 2017. 318 p.
13. The website of the RIA news agency. [electronic resource]. Access mode: https://ria.ru/20221017/predpriyatiya-1824081417.html
14. Telnov, Yu. F. Development of architectures of digital enterprises. Scientific works of the Free Economic Society of Russia. 2021. Vol. 230, No. 4. pp. 230-235. DOI 10.38197/2072-2060-2021-230- 4-230-235. EDN RVLEES.
15. Romashchenko, M.A. Vasilchenko, D.V. Pukhov, D.A. The use of neural network algorithms for visual control of the topology of printed circuit boards. Bulletin of VSTU. 2022. № 3.
16. Konovalova G.I. Conceptual and methodological foundations of the digital transformation of a machine-building enterprise. Production organizer. 2023. № 1.
17. Novikova, V. D. Big data in industry. Modern science: current issues and development prospects: proceedings of the International (correspondence) Scientific and Practical Conference, Sofia, Bulgaria, December 25, 2019. Sofia, Bulgaria: Mir Nauki Scientific Publishing Center (IP Vostretsov Alexander Ilyich), 2019. pp. 225-228. EDN PYARHB.
18. Aylarova, Z. K. Dzakoev, Z. L. Digital modernization of production systems of enterprises. Economics and management: problems, solutions. 2019. Vol. 6, No. 1. pp. 128-132. EDN NXWCHB.
19. Tikhonov, I.A. Intellectualization of electronic industry enterprises and risk management // I.A. Tikhonov, S.S. Antsyferov // Problems of artificial intelligence. - 2023. № 4 (31). - 29-39. - http://search.rads-doi.org/project/14374/object/210540 doi: 10.34757/2413-7383.2023.31.4.004
20. Teplova, O.V. Principles Of Recognizing The Psycho-Emotional State Of A Person In Automated Control Systems // O.V. Teplova, O.A. Krivodubsky // Problems of artificial intelligence. - 2023. № 4

Issue: 3(38)'2025
Section: AUTOMATION AND CONTROL OF TECHNOLOGICAL PROCESSES AND PRODUCTION
How to cite: A. A. Lichman, O. Yu. Cherednikova. AN ALGORITHM FOR INTELLECTUALIZING AUTOMATED CONTROL SYSTEM FAILURE PREVENTION BASED ON PREDICTIVE ANALYTICS // Problems of Artificial Intelligence. - 2025. - № 3 (38). - P. 148-156. - https://paijournal.guiaidn.ru/en/2025/3(38)-13.html

Back to issue 3(38)'2025