Methodology for forming an integral assessment of the reliability of automated control systems of transport infrastructure at the design stage

Transport systems, as objects of automated control, impose a number of specific requirements on the reliability of such control. Firstly, in addition to ensuring the reliability of individual technical components and subsystems, it is necessary to guarantee the security of information flows circulating within the human-machine control loop under conditions of increasing digitalization. Secondly, the same requirements apply to the software being developed. Thirdly, the use of cloud technologies for big data storage necessitates the consideration of protective measures of both technical and algorithmic nature at the design stage. Finally, the functional heterogeneity of a wide range of technical devices included in both the controller and the controlled object requires, at the design stage, the development of a methodology for a generalized, integrated, and predictive assessment of the reliability of the automated control system as a basis for evaluating alternative options for its structure and composition. In this context, the high vulnerability of water transport system facilities to various risks and vulnerabilities, both seasonal and permanent, must be considered. This underscores the relevance of developing a method for constructing an integrated assessment of the reliability of automated control systems for water transport facilities, which is the aim of this study. Within this general task, the following issues are addressed: determining the dimension and structure of the integral indicator; including qualitatively described vulnerabilities inherent in the control object and control loop; constructing a mathematical model of the functioning of the designed automated control system; building, on this basis, a management simulation model to analyze the “bottlenecks” that most significantly impact the assessment; and using probabilistic and theoretical methods of active risk management at the stage of considering alternative options for individual parts of the project. The methodological basis of the study includes the general provisions of applied probability theory, the theory of experimental design on simulation models, and the optimization of the design reliability indicator using mathematical programming tools. The result is a method for constructing an integral indicator of the design reliability of an automated control circuit for large water transport systems, aggregating the probabilities of heterogeneous technical vulnerabilities, information vulnerabilities at cloud interface points, and potential software errors.

1. Нimmelblauу D. М. Applied nonlinear programming. Moskow: Mir, 1975: 536.

2. Gasnikov А. V. Modern Numerical Optimization Methods. Gradient Descent Method. М.: MCNMO, 2021: 272.

3. Yastrebov, I. M. “Risk management in software development in the field of information protection.” Vestnik gosudarstvennogo universiteta morskogo i rechnogo flota im. admirala S. O. Makarova 15.6 (2023): 1105–1114. DOI: 10.21821/2309-5180-2023-15-6-1105-1114.

4. Dahl, O. -J., E. W. Dijkstra, and C.A.R. Hoare Structured Programming. Moscow: Mir, 1975: 247.

5. Goloskokov, K. P., N. B. Glebov and A. A. Astapkovich. “Modeling of transportation and technological processes in automated systems.” Vestnik gosudarstvennogo universiteta morskogo i rechnogo flota im. admirala S. O. Makarova 16.1 (2024): 154–162. DOI: 10.21821/2309-5180-2024-16-1-154-162.

6. Goloskokov, K. P., V. V. Korotkov and A. A. Astapkovich. “Determination of the optimal period for computations control in complexes of automated control systems programs.” Vestnik gosudarstvennogo universiteta morskogo i rechnogo flota im. admirala S. O. Makarova 15.5 (2023): 885–892. DOI: 10.21821/2309-5180-2023-15-5-885-892.

7. Strelavina, O. D., S. N. Efimov, V. A. Terskov and M. A. Likharev. “Increasing software reliability of a distributed control systems.” Sibirskiy aerokosmicheskiy zhurnal 22.3 (2021): 459–467. DOI: 10.31772/2712-8970-2021-22-3-459-467.

8. Naykhanova, L. V., S. V. Dambaeva and M. A. Pykin. “Raschet slozhnosti programmnogo produkta metodom funktsional’nykh tochek.” Nauchnye issledovaniya 1.6(17) (2017): 12–16.

9. Guz, I. D. and V. A. Ostreykovskiy. “Operational reliability analysis of hardware of data storage systems.” Vestnik kibernetiki 3(35) (2019): 35–42. DOI: 10.34822/1999-7604-2019-3-35-42.

10. Zvonareva, A. A. and A. O. Tolokonskiy. “Basic aspects of the reliability of control system software.” Vestnik Natsional’nogo issledovatel’skogo yadernogo universiteta MIFI 10.5 (2021): 429–435. DOI: 10.1134/S2304487X21050126.

11. Mikhalevich, I. F. “Kontseptual’nye problemy transportnoy bezopasnosti vodnykh intellektual’nykh transportnykh sistem.” Dependability 24.2 (2024): 72–87. DOI: 10.21683/1729-2646-2024-24-2-72-87.

12. Netes, V. A. “Typical shortcomings in the dependability-related publications.” Dependability 25.1 (2025): 40–45. DOI: 10.21683/1729-2646-2025-25-1-40-45.

13. Gladkikh, T. D. “The dependability model of the idustrial control system.” Sovremennye naukoemkie tekhnologii 2 (2023): 30–35. DOI: 10.17513/snt.39520.