The Review of Standardized Risk-Oriented Methods and Models to Ensure the Quality Assurance of the System
Abstract
The review focuses on the quality assurance process, which is widely used in the design, operation, modernization and development of various types of quality assurance and confirmation systems. Examples are systems created and functioning in the interests of public authorities and corporations, energy, financial and economic, insurance and industrial structures, the fuel and energy complex, the aerospace industry, emergency services, housing and communal services, etc. The quality assurance process under consideration is one of 30 standardized processes in the systems life cycle according to ISO/IEC/IEEE 15288 "Systems and software engineering ‒ System life cycle processes" and GOST R 57193 "System and software engineering. System life cycle processes" (processes of agreement, organizational support of the project, technical management and technical processes are covered). The review describes the very quality assurance process, defines the role of risk-based methods and models. The possibilities of probabilistic risk analysis for the selection of preventive measures to counter threats are presented, the characteristics of quantitative indicators and typical methods and models for predicting risks, as well as tasks for ensuring quality assurance are given. The risk is defined as a combination of the probability of damage and the severity of the damage to the system.
The ideas outlined in the review are brought to the level of implementation in GOST R 59994-2022 "System Engineering. System analysis of the quality assurance process for the system".
References
2. Kostogryzov A., Nistratov A., Nistratov G. Analytical Risks Prediction. Rationale of System Preventive Measures for Solving Quality and Safety Problems. In: Sukhomlin V., Zubareva E. (eds.) Modern Information Technology and IT Education. SITITO 2018. Communications in Computer and Information Science. Vol. 1201. Cham: Springer; 2020. p. 352-364. doi: https://doi.org/10.1007/978-3-030-46895-8_27
3. Kostogryzov A., Panov V., Stepanov P., Grigoriev L., Nistratov A., Nistratov G. Optimization of sequence of performing heterogeneous repair work for transport systems by criteria of timeliness. 2017 4th International Conference on Transportation Information and Safety (ICTIS). Canada, AB: IEEE Press Banff; 2017. p. 872-876. doi: https://doi.org/10.1109/ICTIS.2017.8047870
4. Kostogryzov A., Stepanov P., Grigoriev L., Atakishchev O., Nistratov A., Nistratov G. Improvement of Existing Risks Control Concept for Complex Systems by the Automatic Combination and Generation of Probabilistic Models and Forming the Storehouse of Risks Predictions Knowledge. In: Proceedings of the 2nd International Conference on Applied Mathematics, Simulation and Modelling (AMSM). Phuket, Thailand: DEStech Publications Inc.; 2017. p. 279-283. doi: https://doi.org/10.12783/dtetr/amsm2017/14857
5. 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). USA, Dallas: Engineering and Technical Management Symposium; 2000. p. 63-70.
6. Kostogryzov A.I., et al. Mathematical Models and Applicable Technologies to Forecast, Analyze, and Optimize Quality and Risks for Complex Systems. In: Proceedings of the First International Conference on Transportation Information and Safety (ICTIS). Wuhan, China: American Society of Civil Engineers; 2011. p. 845-854. doi: https://doi.org/10.1061/41177(415)107
7. Kostogryzov A., Nistratov G., Nistratov A. Some Applicable Methods to Analyze and Optimize System Processes in Quality Management. In: Aized T., ed. Total Quality Management and Six Sigma. London: IntechOpen; 2012. p. 127-196. doi: http://dx.doi.org/10.5772/46106
8. Kostogryzov A., Grigoriev L., Nistratov G., Nistratov A., Krylov V. Prediction and Optimization of System Quality and Risks on the Base of Modelling Processes. American Journal of Operations Research. 2013;3(1A):217-244. doi: https://doi.org/10.4236/ajor.2013.31A021
9. Kostogryzov A., Makhutov N., Nistratov A., Reznikov G. Probabilistic Predictive Modelling for Complex System Risk Assessments. In: Abdalla R., El-Diasty M., Kostogryzov A., Makhutov N.A. (eds.) Time Series Analysis ‒ New Insights. London: IntechOpen; 2022. doi: http://dx.doi.org/10.5772/intechopen.106869
10. Akundi A., Lopez V. A Review on Application of Model Based Systems Engineering to Manufacturing and Production Engineering Systems. Procedia Computer Science. 2021;185:101-108. doi: https://doi.org/10.1016/j.procs.2021.05.011
11. Kołowrocki K., Soszyńska-Budny J. Reliability and Safety of Complex Technical Systems and Processes. Springer Series in Reliability Engineering. London: Springer; 2011. 405 p. doi: https://doi.org/10.1007/978-0-85729-694-8
12. Kołowrocki K., Soszyńska-Budny J. Prediction of critical infrastructures safety. In: The 10th International Conference on Digital Technologies 2014. IEEE Computer Society; 2014. p. 130-138. doi: https://doi.org/10.1109/DT.2014.6868704
13. Zio E. An Introduction to the Basics of Reliability and Risk Analysis. World Scientific Publishing Co Pte Ltd; 2007. 236 p. doi: https://doi.org/10.1142/6442
14. 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. In: 2017 2nd International Conference on System Reliability and Safety (ICSRS). Milan, Italy: IEEE Computer Society; 2017. p. 368-373. doi: https://doi.org/10.1109/ICSRS.2017.8272850
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. p. 298-303. doi: https://doi.org/10.12783/dtcse/cnai2018/24174
16. Kostogryzov A., Grigoriev L., Kanygin P., Golovin S., Nistratov A., Nistratov G. The Experience of Probabilistic Modeling and Optimization of a Centralized Heat Supply System Which is an Object for Modernization. In: International Conference on Physics, Computing and Mathematical Modeling (PCMM). Shanghai: DEStech Publications Inc.; 2018. p. 93-97. doi: https://doi.org/10.12783/dtcse/pcmm2018/23643
17. Artemyev V., Rudenko J., Nistratov G. Probabilistic Methods and Technologies of Risk Prediction and Rationale of Preventive Measures by Using "Smart Systems": Applications to Coal Branch for Increasing Industrial Safety of Enterprises. In: Kostogryzov A., ed. Probabilistic Modeling in System Engineering. London: IntechOpen; 2018. p. 23-51. doi: http://dx.doi.org/10.5772/intechopen.75109
18. Kershenbaum V., Grigoriev L., Kanygin P., Nistratov A. Probabilistic Modeling Processes for Oil and Gas. In: Kostogryzov A. Probabilistic Modeling in System Engineering. London: IntechOpen; 2018. p. 55-79. doi: http://dx.doi.org/10.5772/intechopen.74963
19. Kostogryzov A., Nistratov A., Nistratov G., Atakishchev O., Golovin S., Grigoriev L. The Probabilistic Analysis of the Possibilities to Keep "Organism Integrity" by Continuous Monitoring. In: Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018). Chengdu, China: Atlantis Press; 2018. p. 432-435. doi: https://doi.org/10.2991/mmsa-18.2018.96
20. Kostogryzov A., Korolev V. Probabilistic Methods for Cognitive Solving of Some Problems in Artificial Intelligence Systems. In: Kostogryzov A., Korolev V. (eds.). Probability, Combinatorics and Control. London: IntechOpen; 2019. p. 3-34. doi: http://dx.doi.org/10.5772/intechopen.89168
21. Eid M., Rosato V. Critical Infrastructure Disruption Scenarios Analyses via Simulation. In: Setola R., Rosato V., Kyriakides E., Rome E. (eds.). Managing the Complexity of Critical Infrastructures. Studies in Systems, Decision and Control. Vol. 90. Cham: Springer; 2016. p. 43-61. doi: https://doi.org/10.1007/978-3-319-51043-9_3
22. Kostogryzov A.I. To the Methods of System Engineering: Probabilistic Approaches to the Analysis of the System Quality Management Process. Modern Information Technologies and IT-Education. 2022;18(2):227-240. (In Russ., abstract in Eng.) doi: https://doi.org/10.25559/SITITO.18.202202.227-240
23. Gneiting T., Balabdaoui F., Raferty A.E. Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society. Series B (Statistical Methodology). 2007;69(2):243-268. doi: https://doi.org/10.1111/j.1467-9868.2007.00587.x
24. Meridji K., Issa G. A development approach of software requirements for renewable energy applications using fundamental principles of software engineering. In: 2013 1st International Conference & Exhibition on the Applications of Information Technology to Renewable Energy Processes and Systems. IEEE Computer Society; 2013. p. 107-112. doi: https://doi.org/10.1109/IT-DREPS.2013.6588162
25. Shah L., Siadat A., Vernadat F. Maturity assessment in risk management in manufacturing engineering. In: 2009 3rd Annual IEEE Systems Conference. IEEE Computer Society; 2009. p. 296-301. doi: https://doi.org/10.1109/SYSTEMS.2009.4815815
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication policy of the journal is based on traditional ethical principles of the Russian scientific periodicals and is built in terms of ethical norms of editors and publishers work stated in Code of Conduct and Best Practice Guidelines for Journal Editors and Code of Conduct for Journal Publishers, developed by the Committee on Publication Ethics (COPE). In the course of publishing editorial board of the journal is led by international rules for copyright protection, statutory regulations of the Russian Federation as well as international standards of publishing.
Authors publishing articles in this journal agree to the following: They retain copyright and grant the journal right of first publication of the work, which is automatically licensed under the Creative Commons Attribution License (CC BY license). Users can use, reuse and build upon the material published in this journal provided that such uses are fully attributed.
