Model of Analysis of Digital Twin Application to Predict Incidents and Ensure Occupational Safety in Industry

For citation.
Dokukin A.V., Lomakin M.I., Niyazova Yu.M., Syromyatnikov A.E., Perevezentsev I.G. Model of Analysis of Digital Twin Application to Predict Incidents and Ensure Occupational Safety in Industry. Bezopasnost Truda v Promyshlennosti = Occupational Safety in Industry. — 2025. — № 8. — рр. 39-44. (In Russ.). DOI: 10.24000/0409-2961-2025-8-39-44


Annotation:

The study develops a mathematical system that encompasses the theory of reliability, risk management principles, and predictive analytics to assess the impact of digital twins on equipment reliability and personnel safety. The model incorporates key parameters, including the intensity of failures, recovery rates, and efficiency factors, which quantitatively determine the impact of a digital twin on hazard detection and elimination. 
The proposed model formalizes the interconnection between the implementation of digital twins and the outcomes in the sphere of safety through several interconnected components, which are equipment reliability functions considering both baseline failure rates and those enhanced by digital twins, integrated safety indices uniting the technical reliability with human factor assessment, and optimization functions for the comprehensive cost-benefit analysis of digital twin deployment. 
The key results have demonstrated that digital twins significantly reduce effective failure intensity due to the early detection of malfunctions and possible preventive maintenance. The improvements of the system’s reliability can be quantitatively assessed and show the measurable risk reduction in industrial operations. The model also considers the improvement of maintenance efficiency through the accelerated detection of failures, improved accuracy of diagnostics, and optimized repair processes. 
The study introduces the utility function that balances multiple factors, which are the expenses on accident prevention, implementation expenses, increasing operational efficiency, and general safety improvement. This helps organizations to optimize digital twin strategy deployment based on empirical data. Dynamic Bayesian approaches have been proposed for continuous parameter update in real-time mode based on the accumulated operational data. 
The practical application encompasses predictive maintenance software, hazard virtual modeling for training purposes, safety monitoring systems in real time, and risk assessment protocols. These applications are specifically valuable for high-risk industries, i.e., oil and gas, chemical, and heavy industries. The model provides top managers with quantitative tools to prioritize investments into digital twins based on the demonstrated impact on safety and cost efficiency, supporting the evidence-based safety management strategies in the Industry 4.0 environment

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DOI: 10.24000/0409-2961-2025-8-39-44
Year: 2025
Issue num: August
Keywords : occupational safety mathematical model human factor optimization industry digital twin industrial risks equipment reliability predictive analytics
Authors:
  • Dokukin A.V.
    Dr. Sci. (Econ.), Research Associate, aldokukin@yandex.ru All-Russian Research Institute for Civil Defence of the EMERCOM of Russia (the Federal Science and High Technology Center), Moscow, Russian Federation
  • Lomakin M.I.
    Dr. Sci. (Eng.), Dr. Sci. (Econ.), Prof., Research Associate All-Russian Research Institute for Civil Defence of the EMERCOM of Russia (the Federal Science and High Technology Center), Moscow, Russian Federation
  • Niyazova Yu.M.
    Cand. Sci. (Econ.), Assoc. Prof. of the Department Moscow State University of Geodesy and Cartography, Moscow, Russian Federation
  • Syromyatnikov A.E.
    Cand. Sci. (Econ.), Assoc. Prof. of the Institute of Design & Urban Studies Saint Petersburg Research University of Information Technologies, Mechanics and Optics, Saint Petersburg, Russian Federation
  • Perevezentsev I.G.
    Postgraduate Student Russian State Academy of Intellectual Property, Moscow, Russian Federation