INFORMATION TECHNOLOGIES IN THE DEVELOPMENT OF DIGITAL TWINS FOR ENGINEERING SYSTEMS
DOI:
https://doi.org/10.35546/kntu2078-4481.2025.4.2.4Keywords:
digital transformation, engineering modeling, virtual models, system integration, operational data processing, technical condition monitoring, adaptive control, failure forecasting, life cycle of an object.Abstract
The relevance of the research stems from the intensification of digitalization in engineering systems and the need for well-grounded technical and managerial decisions under conditions of increasing system complexity, dynamic operating modes, and limited time for analysis. Under these circumstances, traditional approaches to the design, monitoring, and operation of engineering systems do not provide adequate adaptability, which highlights the importance of using digital twins as a tool for integrating models, data, and analytical control mechanisms. The purpose of the article is to provide a scholarly synthesis and justification of the role of information technologies in developing digital twins for engineering systems, as well as to identify ways to optimize their application for analysis, monitoring, and control of technical objects throughout the entire life cycle.The research methods include a systematic analysis of modern information technologies, logical and analytical generalization of architectural and functional approaches to building digital twins, and a comparative assessment of practices in digital monitoring, forecasting, and optimization of engineering systems. Research results. The study analyzes the current state and development trends of information technologies used in the creation of digital twins. Architectural solutions and technological components of digital twins are summarized from the standpoint of functional interaction and scalability. The potential of real-time digital twin applications for monitoring, forecasting, and optimizing the performance of engineering systems is examined. Key scientific and practical challenges associated with data quality, model accuracy, cybersecurity, and integration with existing engineering infrastructure are identified. The feasibility of phased and modular implementation of digital twins is substantiated with regard to requirements of efficiency and reliability. Conclusions. It is established that digital twins based on modern information technologies form an applied foundation for adaptive management of engineering systems and contribute to improved analytical accuracy and operational resilience. The study demonstrates that the practical effectiveness of digital twins depends on architectural coherence of components, data quality, and the level of cybersecurity.Future research perspectives are linked to the development of methods for enhancing the robustness of digital models under incomplete and noisy data, integrating digital twins with adaptive analytical algorithms, and forming unified approaches to their application in complex engineering systems.
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