Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-8· 0 citations· 66 references
Abstract
This paper investigates digital twins as key components of cyber-physical supervision architectures for industrial systems through a systematic analysis of 65 publications (2019-2026). A tripartite taxonomy physical first, data-driven, and hybrid approaches is established and evaluated using operational criteria including data requirements, robustness to process drift, interpretability, and decision latency. The study highlights the evolution of digital twins transitioning from virtual replicas toward intelligent systems through the synergy and the integration of physical models, measured data, and artificial intelligence. Despite these advances, a gap remains between academic developments and industrial deployment due to sensitivity to data quality, limited explainability, and interoperability challenges. Six cross-cutting challenges are identified architectural standardization, physics informed robustness, self-supervised learning, explainable AI, cybersecurity, and scientific reproducibility to support the development of autonomous and prescriptive digital twins for resilient Industry 4.0/5.0 supervision systems. This work contributes through a supervision-oriented perspective combining a unified taxonomy,and quantitative benchmarking framework of digital twins.
This study evaluates the quantitative impact of DT implementation, highlighting significant gains in predictive maintenance, production efficiency, and design cycle reduction, and underscores the necessity for standardized data protocols and secure, distributed DT ecosystems to unlock the full potential of cyber-physic...
Meta-Twins is proposed, a system-of-systems digital twin for data engineering infrastructure, where engineering intent is represented as normalized relational metadata (mirrored state) and continuously enforced by persistent daemon processes (autonomous behavior).
Zhong-Yuan T. Lee, Arvin Shadravan, Hamid R. Parsaei· Human-Intelligent Systems In...· 0 citations
This systematic review explores the intersection between Multi-Agent Systems and Digital Twins, with a particular focus on predictive maintenance applications in resource-constrained contexts and reveals that, despite significant progress, no existing system offers an integrated embedded-distributed hierarchical soluti...
Korota Arsène Coulibaly, M. Hamlich· arXiv.org· 0 citations
The proof of concept shows how telemetry, data storage, machine learning models, and operator feedback can be combined to support auditable, explainable, and human-contestable industrial decisions, demonstrating the classification accuracy, remaining useful life forecasting capabilities, and enhanced recommendation pre...
Luis Ferreira, E. Gonçalves, G. Putnik et al.· Sustainability· 0 citations
A comprehensive meta-analysis of how DT technologies contribute to the realization of Industry 5.0 objectives across domains such as Smart Additive Manufacturing, Predictive Maintenance, Cyber-Physical Cognitive Systems, Intelligent Supply Chain, and Adaptive Scheduling reveals that, while SAM exhibits the highest Indu...
Swati Lipsa, R. K. Dash, Korhan Cengiz et al.· PeerJ Computer Science· 0 citations
This paper proposes a unified reference architecture for pipeline digital twins, supported by a mathematical formulation of synchronization and a comparative synthesis of existing approaches, and outlines future research directions, including the development of hybrid modeling techniques, edge computing integration, an...
Hamed Azimi, Rahim Shoghi, H. Shiri· Technologies· 1 citation
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