DIGITAL TWINS IN INDUSTRY: ARCHITECTURE, APPLICATION AND ECONOMIC EFFICIENCY
Authors:
1.
Voronezh State University of Forestry and Technologies named after G.F. Morozov
Russian Federation
Russian Federation
2.
Voronezh State University of Forestry and Technologies named after G.F. Morozov
Russian Federation
Russian Federation
3.
Voronezh State University of Forestry and Technologies named after G.F. Morozov
Russian Federation
Russian Federation
4.
Voronezh State University of Forestry and Technologies named after G.F. Morozov
Russian Federation
Russian Federation
Type:
Сonference article
Pages:
from 199
to 204
Published:
30.12.2025
Subject area:
UDC 004.9
Language:
Russian
Keywords:
digital twins, Industry 4.0, predictive maintenance, IoT, industrial analytics, digital transformation, virtual modeling, cyber-physical systems
Abstract:
The article provides a comprehensive analysis of the concept of digital twins and their practical application in modern industry. The study covers the architectural features of digital twins, including the physical layer, the data and integration layer, the modeling layer, and the service layer. The technologies underlying digital twins are considered in detail: IoT sensors, cloud platforms, machine learning and physical and mathematical modeling. Using a practical example of forecasting the maintenance of a pumping station, the advantages of switching from planned to predictive maintenance are demonstrated. The results of the study show that the introduction of digital twins can reduce maintenance costs by 20-30% by preventing unplanned downtime and optimizing resources. The article is valuable for specialists in the field of industrial automation, digital transformation and management of production assets.
The article provides a comprehensive analysis of the concept of digital twins and their practical application in modern industry. The study covers the architectural features of digital twins, including the physical layer, the data and integration layer, the modeling layer, and the service layer. The technologies underlying digital twins are considered in detail: IoT sensors, cloud platforms, machine learning and physical and mathematical modeling. Using a practical example of forecasting the maintenance of a pumping station, the advantages of switching from planned to predictive maintenance are demonstrated. The results of the study show that the introduction of digital twins can reduce maintenance costs by 20-30% by preventing unplanned downtime and optimizing resources. The article is valuable for specialists in the field of industrial automation, digital transformation and management of production assets.
Keywords:
digital twins, Industry 4.0, predictive maintenance, IoT, industrial analytics, digital transformation, virtual modeling, cyber-physical systems
digital twins, Industry 4.0, predictive maintenance, IoT, industrial analytics, digital transformation, virtual modeling, cyber-physical systems
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