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.
Digital twin (DT) technology has emerged as a cornerstone of Industry 4.0, facilitating real-time synchronization between physical assets and virtual models to drive operational excellence. Unlike prior surveys that address singular industrial domains, presenting qualifications in general terms without paradigm-to-task mapping, this review uniquely synthesizes DT architectural maturation across Technology Readiness Levels (TRLs) 1 through 9, quantitative performance outcomes from 39 documented industrial implementations spanning 10 sectors, and an explicit algorithmic taxonomy mapping distinct AI paradigms to specific functional DT requirements. By synthesizing empirical data across the aerospace, automotive, and manufacturing sectors, this study evaluates the quantitative impact of DT implementation, highlighting significant gains in predictive maintenance, production efficiency, and design cycle reduction. This research further examines the synergistic role of Machine Learning (ML) paradigms integrated within DT systems, specifically, physics-informed neural networks (PINNS), generative adversarial networks (GANs), deep transfer learning, reinforcement learning, and federated learning, in enhancing diagnostic accuracy and enabling autonomous decision-making. Despite these advancements, this review identifies critical barriers in data interoperability, cybersecurity, and workforce expertise that impede widespread adoption. This paper concludes by outlining future research directions, emphasizing the necessity for standardized data protocols and secure, distributed DT ecosystems to unlock the full potential of cyber-physical integration in a data-driven industrial landscape.
Digital twins have become foundational in cyber-physical systems by mirroring asset state and enabling monitoring, simulation, and optimization. However, the data infrastructure that powers modern analytics and AI—including transformation logic, orchestration state, lineage, and data quality rules—is typically encoded across fragmented scripts, configuration artifacts, and logs, making it difficult to audit, evolve, or safely automate. This paper proposes Meta-Twins, 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). Meta-Twins exposes a SQL-native control plane for defining transformations, dependencies, schedules, execution history, and quality gates, thereby decoupling semantics from tool-specific execution mechanics and providing a bounded interface for safe AI-assisted updates. We implement a prototype on Databricks to demonstrate feasibility and portability of the design, and we evaluate Meta-Twins through a structured comparison with procedural pipelines and declarative modeling tools across coupling, transparency, self-service readiness, and resilience. The results indicate that representing infrastructure intent as relational state enables consistent auditability, deterministic execution, and reduced operational coupling, supporting human-centric and resilient data engineering aligned with the conceptual goals of Industry 5.0, particularly in terms of transparency and human-centric interaction.
Z. 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 solution that simultaneously meets the requirements of Industry 5.0.
Industrial operations increasingly face high-stakes decisions that involve people, data streams, simulations, and control systems. Urgent sessions often require external expertise, retrieval of documents and live telemetry, running what-if simulations, and verifying safety constraints. These scenarios highlight the need for secure interoperability, explainable decision support, and human-in-the-loop control. This paper presents a proposal of a technology-agnostic reference architecture that builds on Industry 4.0 frameworks by incorporating the human-centric, resilient, and sustainable principles of Industry 5.0. Its intelligent layer enables the new approach to human involvement in the process, facilitating meaningful human–machine collaboration. The proposed research provides a practical and conceptual framework for systems engineers, industrial software architects, and operations managers seeking to transition legacy operational plants into human-aligned ecosystems. Its feasibility is evaluated through a simulation-based underground mining testbed, where heterogeneous data sources and communication protocols are integrated into a common operational environment. 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 precision enabled by iterative operator feedback loops.
Luis Ferreira, Eduardo Gonçalves, G. Putnik et al.· Sustainability· 0 citations
Industry 5.0 represents a paradigm shift toward human-centric, intelligent, and sustainable manufacturing systems. At the core of this transformation lies the Digital Twin (DT), a virtual replica of physical assets that enables real-time monitoring, simulation, and decision-making. This article presents a comprehensive meta-analysis of how DT technologies contribute to the realization of Industry 5.0 objectives across domains such as Smart Additive Manufacturing (SAM), Predictive Maintenance (PM), Cyber-Physical Cognitive Systems (CPCS), Intelligent Supply Chain (ISC), and Adaptive Scheduling (AS). By analyzing 125 peer-reviewed studies, we quantify the feature-wise attainment levels of Industry 5.0 and identify critical gaps in current implementations. The findings reveal that, while SAM exhibits the highest Industry 5.0 readiness, other features, such as cognitive systems, remain underdeveloped. The article concludes by outlining key research challenges and presenting a strategic roadmap to advance the real-world integration of DTs within Industry 5.0 frameworks.
Swati Lipsa, R. K. Dash, Korhan Cengiz et al.· PeerJ Computer Science· 0 citations
Digital Twin (DT) technology has emerged as a transformative approach in pipeline engineering, enabling real-time monitoring, predictive analytics, and enhanced decision-making across the asset lifecycle. This review critically examines recent advancements in the application of digital twins for pipeline systems, with a particular focus on condition monitoring, leak detection, corrosion assessment, and predictive maintenance. The study synthesizes findings from a wide range of literature to identify key enabling technologies, including Internet of Things (IoT) sensors, data-driven modeling, computational fluid dynamics (CFD), and machine learning algorithms. Special attention is given to the integration of physics-based and data-driven models for improving the accuracy and reliability of digital twin frameworks. In addition, 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. The review highlights how digital twins facilitate early fault detection and operational optimization by continuously synchronizing physical assets with their virtual counterparts. The review also emphasizes the importance of uncertainty-aware and reliability-informed digital twin frameworks for robust decision-making in safety-critical pipeline applications. Applications in subsea, oil and gas, and water distribution pipelines are explored, demonstrating the versatility of DT systems under different environmental and operational conditions. Despite significant progress, challenges remain in data integration, model validation, scalability, and cybersecurity. Furthermore, the lack of standardized architectures and interoperability frameworks limits widespread adoption. This paper concludes by outlining future research directions, including the development of hybrid modeling techniques, edge computing integration, and AI-driven autonomous decision systems. Overall, digital twin technology represents a paradigm shift in pipeline engineering, offering substantial potential to enhance safety, efficiency, and sustainability in complex infrastructure systems.
Hamed Azimi, Rahim Shoghi, H. Shiri· Technologies· 0 citations
