Methodological approach to probabilistic prediction and comparison of systems operation quality under conditions of uncertainty
https://doi.org/10.21683/1729-2646-2024-24-1-10-24
Abstract
Abstract. Aim. To propose a methodological approach to probabilistic forecasting and comparison of the performance of systems producing material and/or information products, to illustrate the practicality of the proposed approach with examples in various applications. Methods. Methods and models based on the methods of probability theory and system analysis, brought to implementation in national standards of system engineering, are proposed for use. Results. Models of complex systems producing material and/or information products are adapted for predicting and comparing for the same system under different operating conditions, for different systems applied to the same time period or for different time periods with the same or different duration and operating conditions. The proposed approach covers methods for assessing the relative part of the system functions performed with acceptable quality, estimating costs in the life cycle of systems, assessing the relative degree of satisfaction of stakeholders associated with quality and costs in system operation. Conclusions. The efficiency of the proposed methodological approach to probabilistic forecasting and comparison of the performance of systems of various applications under conditions of uncertainty is demonstrated. The approach can be used for system analysis and optimisation of the performance of systems producing material and/or information products, substantiation of quantitative system requirements and engineering solutions aimed at meeting the needs of stakeholders.
About the Authors
A. I. Kostogryzov
Federal Research Center “Computer Science and Control”
of the Russian Academy of Sciences
Russian Federation
Russian Federation
Andrey I. Kostogryzov, Dr., Prof., Chief Researcher
44, bld.2 Vavilova St., Moscow, Russia, 119333
A. A. Nistratov
Federal Research Center “Computer Science and Control”
of the Russian Academy of Sciences
Russian Federation
Russian Federation
Andrey A. Nistratov, Ph.D. (Eng.), Senior Researcher
44, bld.2 Vavilova St., Moscow, Russia, 119333
References
1. Kostogryzov A., Kanygin P., Nistratov A. Probabilistic comparisons of systems operation quality for uncertainty conditions. RTA&A 2020;1(56);15:63-73 https://doi. org/10.24411/1932-2321-2020-11007.
2. Kostogryzov A. Estimations of “Smart” Engineering Systems Operation by Probabilistic Measures of Correctness and Reliability. RTA&A 2020;3(58);15:19-35 https://doi. org/10.24411/1932-2321-2020-13002.
3. Kostogryzov A.I., Petuhov A.V., Scherbina A.M. [Foundations of evaluation, providing and increasing output information quality for automatized system]. Moscow: Vooruzheniye, politika i konversiya; 1994. (in Russ.)
4. Kostogryzov A.I. Software Tools Complex for Evaluation of Information Systems Operation Quality (CEISOQ). In: Proceedings of the 34-th Annual Event of the Government Electronics and Information Association (GEIA), Engineering and Technical Management Symposium. Dallas (USA); 2000. Pp. 63-70.
5. Bezkorovainy M.M., Kostogryzov A.I., Lvov V.M. Modelling Software Complex for Evaluation of Information Systems Operation Quality CEISOQ. 150 problems of analysis and synthesis and examples for their solutions. Moscow: Vooruzheniye, politika i konversiya; 2001. (in Russ.)
6. Kostogryzov A., Nistratov G. Standardization, mathematical modelling, rational management and certification in the field of system and software engineering (80 standards, 100 mathematical models, 35 software tools, more than 50 practical examples). Moscow: Vooruzheniye, politika i konversiya; 2004. (in Russ.)
7. Kostogryzov A.I., Stepanov P.V. Innovative management of quality and risks in systems life cycle. Moscow: Vooruzheniye, politika i konversiya; 2008. (in Russ.)
8. Zio, E. An Introduction to the Basics of Reliability and Risk Analysis. World Scientific Publishing Co. Pte. Ltd.; 2006.
9. Kolowrocki, K. and Soszynska-Budny, J. Reliability and Safety of Complex Technical Systems and Processes. Springer-Verlag London Ltd.; 2011.
10. Kostogryzov A., Nistratov G., Nistratov A. Some Applicable Methods to Analyze and Optimize System Processes in Quality Management. In: Total Quality Management and Six Sigma; 2012. Pp. 127-196.
11. Akimov V., …, Mahutov N. at al. Mahutov N.A., editor. [Security of Russia. Legal, Socioeconomic and Scientific Aspects. The Scientific Foundations of Technogenic Safety]. Moscow: Znanie; 2015. (in Russ.)
12. Eid M., Rosato V. Critical Infrastructure Disruption Scenarios Analyses via Simulation. Managing the Complexity of Critical Infrastructures. A Modelling and Simulation Approach. SpringerOpen; 2016. Pp. 43-62.
13. Artemyev V., Kostogryzov A., Rudenko Ju., Kurpatov O., Nistratov G., Nistratov A. Probabilistic methods of estimating the mean residual time before the next parameters abnormalities for monitored critical systems. In: Proceedings of the 2nd International Conference on System Reliability and Safety (ICSRS). Milan (Italy); 2017. Pp. 368-373.
14. Kostogryzov A., Atakishchev O., Stepanov P., Nistratov A., Nistratov G., Grigoriev L. Probabilistic modelling processes of mutual monitoring operators actions for transport systems. In: Proceedings of the 4th International Conference on Transportation Information and Safety (ICTIS). Banff (Canada); 2017. Pp. 865-871.
15. Kostogryzov A., Grigoriev L., Golovin S., Nistratov A., Nistratov G., Klimov S. Probabilistic Modeling of Robotic and Automated Systems Operating in Cosmic Space. In: Proceedings of the International Conference on Communication, Network and Artificial Intelligence (CNAI). Beijing (China). DEStech Publications, Inc.; 2018. Pp. 298-303.
16. Artemyev V., Rudenko Ju., Nistratov G. Probabilistic modeling in system engineering. Probabilistic methods and technologies of risks prediction and rationale of preventive measures by using “smart systems”. Applications to coal branch for increasing Industrial safety of enterprises. IntechOpen; 2018. Pp. 23-51. http://www.intechopen.com/books/probabilistic-modeling-in-system-engineering.
17. Kershenbaum V., Grigoriev L., Kanygin P., Nistratov A. Probabilistic modeling in system engineering. Probabilistic modeling processes for oil and gas systems. IntechOpen; 2018. Pp. 55-79. http://www.intechopen.com/books/probabilistic-modeling-in-system-engineering.
18. Kostogryzov A., Panov V., Grigoriev L., Zubarev I., Nistratov A., Nistratov G. Estimation of stakeholders satisfaction in application to socially significant systems. In: Proceedings of the Proceedings of the 6th International Conference Actual Problems of System and Software Engineering. Moscow (Russia); 12-14 November, 2019. Pp. 10-16.
19. Kostogryzov A., Korolev V. Probability, combinatorics and control. Probabilistic Methods for Cognitive Solving of Some Problems in Artificial Intelligence Systems. IntechOpen; 2019. Pp. 3-34. DOI: 10.5772/intechopen.89168.
20. Nistratov A.A. Analytical prediction of the integral risk of violation of the acceptable performance of the set of standard processes in a life cycle of highly available systems. Part 1. Mathematical models and methods. Highly Available Systems 2021;17:16-31. DOI: 10.18127/j20729472-202103-02. (in Russ.)
21. Nistratov A.A. Analytical prediction of the integral risk of violation of the acceptable performance of the set of standard processes in a life cycle of highly available systems. Part 2. Software-technological solutions. Examples. Highly Available Systems 2022;18:42-57. DOI: 10.18127/j20729472-202202-03. (in Russ.)
22. Kostogryzov A., Makhutov N., Nistratov A., Reznikov G. Time Series Analysis – New Insights. Probabilistic predictive modeling for complex system risk assessments. IntechOpen; 2023. DOI: 10.5772/intechopen.106869.
Review
For citations:
Kostogryzov A.I., Nistratov A.A. Methodological approach to probabilistic prediction and comparison of systems operation quality under conditions of uncertainty. Dependability. 2024;24(1):10-24. (In Russ.) https://doi.org/10.21683/1729-2646-2024-24-1-10-24
Views:
304
ISSN 1729-2646 (Print)
ISSN 2500-3909 (Online)
ISSN 2500-3909 (Online)
Email this article 