A.A. Nistratov

FRC «Computer Science and Control» RAS (Moscow, Russia)

With the widespread adoption and development of the process approach, it became clear that the standard processes used in the life cycle of highly available systems undoubtedly have a cumulative impact on risks that arise. However, the possibilities for predicting risks in practice are significantly limited: private and integral risks of violation of the acceptable performance of implemented processes, estimated by simplified methods, do not reflect the real picture, and specialized models of specific systems and processes require painstaking and long-term scientific and methodological study. Thus, there was a critical methodological contradiction between objective needs and real capabilities in predicting private and integral risks. Carrying out a scientific search for ways to eliminate the identified contradiction, the main goal of this work (in two parts) is to create scientifically based methodological and software-technological solutions for analytical prediction of the integral risk of violation of the acceptable performance of a given set of standard processes in the life cycle of systems. In the first part of the work, for 30 standard processes defined by GOST R 57193 (characterized by typical actions and real or hypothetical input data for modeling and linked to possible scenarios for their use in the creation and/or operation and/or disposal of systems), mathematical models and methods for predicting the integral risk of violating the acceptable performance of a given set of standard processes with the possibility of traceable analytical dependence on influencing factors are proposed. The second part of the work is devoted to the description of the proposed software-technological solutions for risks prediction using models and methods of the first part for solving practical problems of system engineering.

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.  V. 17. № 3. P. 16−31. DOI: https://doi.org/10.18127/j20729472-202103-02 (in Russian)

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12. Kostogryzov A., Nistratov A., Nistratov G. Some Applicable Methods to Analyze and Optimize System Processes in Quality Management. In Tech. 2012. P. 127−196. URL = http://www.intechopen.com/books/total-quality-management-and-six-sigma/some-applicablemethods-to-analyze-and-optimize-system-processes-in-quality-management.
13. Grigoriev L., Kostogryzov A., Krylov V., Nistratov A., Nistratov G. Prediction and optimization of system quality and risks on the base of modelling processes. American Journal of Operation Researches. Special Issue. 2013. V. 1. P. 217−244. http://www.scirp.org/journal/ajor/.
14. Kostogryzov A., Stepanov P., Nistratov A., Nistratov G., Atakishchev O., Kiselev V. Risks Prediction and Processes Optimization for Complex Systems on the Base of Probabilistic Modeling. Proceedings of the 2016 International Conference on Applied Mathematics,

15. Simulation and Modelling (AMSM2016), Beijing, China.May 28-29. 2016. Р.186-192. www.dropbox.com/s/a4zw1yds8f4ecc5/AMSM2016%20Full%20Proceedings.pdf?dl=0

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17. Artemyev V., Kostogryzov A., Rudenko J., Kurpatov O., Nistratov G., Nistratov A. Probabilistic methods of estimating the mean residual time before the next parameters abnormalities for monitored critical systems. Proceedings of the 2nd International Conference on System Reliability and Safety (ICSRS- 2017). Milan, Italy. December 20-22 2017. Р. 368-373.
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20. Mohamed Eid. Reliability based models to support risk management decision making. Safety and Reliability of Systems and Processes. Gdynia Maritime University. 2020. Р. 77-90. https://ssars.umg.edu.pl/  
21. Kolowrocki K. Safety analysis of multistate ageing car wheel system with dependent components. Safety and Reliability of Systems and Processes. Gdynia Maritime University. 2020. Р. 101-116. https://ssars.umg.edu.pl/
22. Kostogryzov A., Nistratov A. Probabilistic methods of risk predictions and their pragmatic applications in life cycle of complex systems.
23. Safety and Reliability of Systems and Processes. Gdynia Maritime University. 2020. Р. 153-174. https://ssars.umg.edu.pl/