Model of stability provision and methodology for assessing the stability of big technical systems during operation

K.Z. Bilyatdinov


Methodological solutions for improving the management of sustainable operation of big technical systems in adverse conditions are presented. The article proposes a model of stability of big technical systems in a form of a set of interdependent tables and criteria of systems’ stability that describe a system’s condition under the destructive influence. The model describes states of a system’s stability by means of unified tables. The model presents dynamics of a state of stable functioning of a system based on changes of values of the quality indicators. The model forms basic data for its systematic application in the methodology to calculate a complex indicator of a system’s stability. The method described in the article presents an approach to assessment of a technical system’s stability based on the calculation of the criterion of effectiveness of a complex system when dividing the system’s elements into three functional groups. To apply the method on practice the author proposes to use a specially designed software. The main positive effect from the application of the proposed method is a considerable decrease of time and resources needed for assessing stability, for modeling the processes of ensuring the stability of systems and a possibility of software realization of a rational processing of information in the process of management of big technical systems’ maintenance.

Full Text:

PDF (Russian)


Biliatdinov K.Z., Meniailo V.V. Modified method DEA and methodology of technical systems effectiveness assessment // Information technologies, No. 11, 2020. P. 611-617. (in Russian)

Biliatdinov K.Z. Мethodology for assessing the stability of technical systems // Scientific and technical Volga region bulletin. No. 10, 2020. – P. 25 – 28. (in Russian)

Bilyatdinov K.Z., Krivchun Е.А. Development and improvement of assessment means of technical systems quality in the process of maintenance // Proceedings of the 9th International Conference «Distributed Computing and Grid Technologies in Science and Education» (GRID'2021), Dubna, Russia, July 5-9, 2021. Vol. 3041, р. 579-583.

Liapunov A.M. General problem of motion stability. М.; L.: ONTI, 1935. – 386 с.

Dorf R. Sovremennye sistemy upravlenija [Modern systems of control]. Moscow: Laboratorija Bazovyh Znanij [Laboratory of basic knowledge], 2012. 832 p. (In Russian)

Baker J., Henderson S. The Cyber Data Science Process. The Cyber Defense Review. 2017. Vol. 2. No. 2. Pp. 47-68.

Banker R., Kotarac K., Neralić L. Sensitivity and stability in stochastic data envelopment analysis. The Journal of the Operational Research Society. 2015. Vol. 66. No. 1. Pp. 134-147.

Chen J.-X. Overall performance evaluation: new bounded DEA models against unreachability of efficiency. The Journal of the Operational Research Society. 2014. Vol. 65. No. 7. Pp. 1120-1132.

Danyk Yu., Maliarchuk T., Briggs C. Hybrid War: High-tech, Information and Cyber Conflicts. Connections. 2017. Vol. 16. No. 2. Pp. 5-24.

Downes C. Strategic Blind–Spots on Cyber Threats, Vectors and Campaigns. The Cyber Defense Review. 2018. Vol. 3. No. 1. Pp. 79-104.

Han P., Wang L., Song P. Doubly robust and locally efficient estimation with missing outcomes. Statistica Sinica. 2016. Vol. 26. No. 2. Pp. 691-719.

Jabbour K., Poisson J. Cyber Risk Assessment in Distributed Information Systems. The Cyber Defense Review. 2016. Vol. 1. No. 1. Pp. 91-112.

Kalimoldayev M., Abdildayeva A., Mamyrbayev O. Information system based on the mathematical model of the EPS. Open engineering. 2016. Vol. 6. No. 1. Pp. 464-469.

Cox, T., Lowrie, K. Improving Risk Management of Complex Systems // Risk Analysis. 2021. 41(1). P. 1-2.

Karagiannis G. On structural and average technical efficiency. Journal of Productivity Analysis. 2015. Vol. 43. No. 3. Pp. 259-267.

Leys N. Autonomous Weapon Systems and International Crises. Strategic Studies Quarterly. 2018. Vol. 12. No. 1. Pp. 48-73.

Luetje A., Wohlgemuth V. Tracking Sustainability Targets with Quantitative Indicator Systems for Performance Measurement of Industrial Symbiosis in Industrial Parks. Administrative sciences. 2020. Vol. 10. No. 1.

Price M., Walker S., Wiley W. The Machine Beneath: Implications of Artificial Intelligence in Strategic Decision making. PRISM. 2018. Vol. 7. No. 4. Pp. 92-105.

Price M., Walker S., Wiley W. The Machine Beneath: Implications of Artificial Intelligence in Strategic Decision making. PRISM. 2018. Vol. 7. No. 4. Pp. 92-105.

Putz M., Wiene, T., Pierer A. A multi-sensor approach for failure identification during production enabled by parallel datamonitoring. CIRP annals-manufacturing technology. 2018. Vol. 67. No. 1. Pр. 491-494.

Segal A. Bridging the Cyberspace Gap: Washington and Silicon Valley. PRISM. 2017. Vol. 7. No. 2. Pp. 66-77.

Trevino M. Cyber Physical Systems: The Coming Singularity. PRISM. 2019. Vol. 8. No. 3. Pp. 2-13.

Dmitrieva O.N. Optimal control in the model of the use of forest plantations // Application of functional analysis in approximation theory: Sat. scientific tr. Tver: TVGU, 2005. - p. 172-178.

Dmitrieva O.N. Stochastic model of the dynamics of development of forest plantations // Collection of scientific papers "Multilevel system of training specialists based on information and communication technologies of education". Tver: TVGU, 2006. - p. 41-49.

Li Y., Huang Sh., Li, H. Application of phase sequence exchange in emergency control of a multi-machine system // International journal of electrical power & energy systems. 2020. Vol.‏ 121. Article 106136.

Liang Ya., Gao Zh., Gao J. A new method for multivariable nonlinear coupling relations analysis in complex electromechanical system // Applied soft computing. 2020. Vol. 94. Article 106457.


  • There are currently no refbacks.

Abava  Absolutech Convergent 2020

ISSN: 2307-8162