Digital Twin Technology Research Articles

Enhancing 5G energy efficiency through digital twin networks: A comprehensive review

AbstractIn the era of 5G, seamless mobility handovers are critical in densely populated regions like Malaysia to mitigate disruptions and inefficiencies. 5G networks offer unprecedented data speeds and reliability, essential for advancing mobile communication and Internet of Things applications. However, ensuring continuous connectivity and service during mobility remains challenging, especially in urban settings. Digital twin technology presents a promising solution to enhance 5G handover mechanisms. A digital twin network (DTN) mirroring Malaysia's 5G infrastructure is proposed, utilising real‐time data and user behaviour insights to optimise energy consumption during handovers. The focus is on energy‐efficient protocols and algorithms, reviewed through a systematic literature review. The DTN aims to enhance mobility handover efficiency through predictive handovers and adaptive resource allocation, bolstered by sustainable practices such as edge computing. The potential of DTNs to optimise 5G handover processes is explored, starting with the foundational concepts of 5G mobility and digital twins, highlighting the need for improved strategies in high‐mobility scenarios. Methodologies leveraging digital twins to predict network conditions, simulate handover scenarios, and proactively manage decisions are examined, reducing latency and packet loss. Case studies demonstrate how digital twins adapt dynamically to network changes and user mobility, thereby improving quality of service and user experience. Malaysia's specific 5G mobility challenges are addressed with a tailored DTN emphasising energy efficiency, evaluated through practical applications. Evaluation criteria assess effectiveness with in‐depth analysis of methods, performance metrics, limitations, and recommendations for future research. Challenges and future directions including scalability, security, and real‐time data processing, are discussed, aiming to integrate digital twin technology with 5G networks for enhanced connectivity. This abstract provides a roadmap for leveraging digital twins to optimise 5G network performance sustainably, guiding future research and implementation strategies.

Online demand peak shaving with machine‐learned advice in digital twins

AbstractAs the use of physical instruments grows, control algorithms are being increasingly deployed to enhance efficiency and reliability through digital twin technology. Demand load management is central to energy systems within digital twins, which significantly impacts operational costs. Peak demand loads can lead to substantial monthly utility expenses without proper management. AMPAMOD, a randomised online algorithm incorporating machine‐learned insights is introduced to optimise battery operations and mitigate peak demand loads. AMPAMOD leverages limited‐bit information from machine learning models to inform its online decision‐making process for cost‐effective load management. We provide theoretical evidence demonstrating that AMPAMOD maintains minimal advice complexity, has a linear computational cost, and achieves a bounded competitive ratio. Extensive trace‐driven experiments with real‐world household data reveal that AMPAMOD successfully reduces peak loads by over 90%, outperforming other benchmarks by at least 50%. These experimental findings align with our theoretical assertions, showcasing the effectiveness of AMPAMOD.

Monitoring and control of air filtration systems: Digital twin based on 1D computational fluid dynamics simulation and experimental data

This study presents the development of a digital model based on one-dimensional computational fluid dynamics for the monitoring and control of filtering systems used for removing flour, dust, and other particulates from the airflow arriving from various sections of industrial production plants.Focusing on a pilot plant equipped with a cyclone bag filter, historical experimental data was integrated with the results of a one-dimensional fluid dynamics simulation model to create a digital twin capable of real-time control and regulation of industrial plants. In particular, measured pressure drop data under different clogging conditions were interpolated to generate the characteristic curves of the filter under various clogging conditions, to be implemented within the digital model of the plant. The generated model, validated through a dedicated experimental campaign, accurately predicted the airflow rate and pressure distribution across the plant. The system’s capability to adapt to changing operational conditions, such as clogging, was demonstrated through simulation, highlighting the model’s utility in maintaining the desired operation levels while minimizing the need for extensive sensor networks.The analyzed case study in the field of air filtration systems aims to fill the gap in the scientific literature related to the application of Digital Twin technology to the control of industrial manufacturing plants. The findings highlight the potential of digital twins in monitoring and control, as well as predictive maintenance, of industrial systems. The findings highlight the potential of Digital Twins in monitoring and control, as well as predictive maintenance, of industrial systems. Future research activities will explore the model’s applicability in failure and anomaly detection, to further enhance predictive maintenance of air filtering systems.

Promoting digital twin technology application for process industry: A novel generation modelling platform and its implementations

AbstractOne key concept of Industry 4.0 is the industrial digital twin (DT)—a virtual replica of physical assets, processes, and systems. DTs optimise operations, enhance productivity, and facilitate innovation across various industries, including manufacturing, energy, healthcare, and transportation. This paper presents a multi‐domain and multi‐time scale modelling and simulation platform, featuring built‐in model libraries that represent diverse physical, chemical, and behavioural properties. A comprehensive system modelling approach and a closed‐loop industrial control system based on DTs are developed, showcasing advanced control, optimisation, and system security research capabilities. The architecture and advantages of the novel generation modelling platform are detailed, with specific applications highlighted, underscoring its contributions to the process industry.

Transfer-AE: A novel autoencoder-based impact detection model for structural digital twin

Accurately detecting the location and intensity of impacts is crucial for ensuring structural safety. Currently, AI-based structural impact detection methods are widely used for their excellent detection accuracy. However, their generalization capability is limited by the scenarios present in the training data. Many complex and dangerous impact scenarios are difficult to conduct real-world experiments on to collect sufficient samples. To capture all impact scenarios and fully leverage the advantages of AI-based detection technologies, advanced methods involve combining real-world structural monitoring data with corresponding numerical models to construct digital twins. These methods continuously refine the created numerical models with limited real-world data and provide diverse impact scenarios through numerical model simulations. However, there are inevitable differences between digital models and physical models that are challenging to correct through mechanical means. This discrepancy in data distribution between the two models significantly hinders the application of digital twin technology in impact/event identification tasks. To address this challenge, this study proposes a novel model based on autoencoders, named Transfer-AE. Transfer-AE encodes the common features of digital twins in the latent space to bridge the uncertainty gap at a macro scale between numerical models and physical models and synchronously fits the magnitude and location of the impact load in the decoder. This enables consistent detection results for the same impact event, whether the sample comes from the numerical model or the physical model. Transfer-AE includes two operating modes: Mode 1 has a fixed computational complexity with stable inference speed, but the training cost and difficulty increase with data distribution. Mode 2's computational complexity increases with data distribution, but it has a fixed training cost and speed. In both cases involving the geodesic dome structure simulating a deep space habitat and the IASC-ASCE benchmark structure, Transfer-AE demonstrated the best performance in impact localization and quantification tasks compared to mainstream domain-adaptive transfer models.

A digital twin system for centrifugal pump fault diagnosis driven by transfer learning based on graph convolutional neural networks

In industrial sectors such as shipping, chemical processing, and energy production, centrifugal pumps often experience failures due to harsh operational environments, making it challenging to accurately identify fault types. Traditional fault diagnosis methods, which heavily rely on existing fault datasets, suffer from limited generalization capabilities, especially when substantial labeled and specific fault sample data are lacking. This paper proposes a novel fault diagnosis approach for centrifugal pumps, utilizing a digital twin (DT) framework powered by a graph transfer learning model to address this issue. Firstly, a high-fidelity DT model is constructed to simulate the flow-induced vibration response of the impeller under different health states to enrich the type and scale of the dataset. Secondly, a graph convolutional neural networks (GCN) model is constructed to learn the knowledge of simulation data, and the Wasserstein distance between simulation data and measured data is optimized for adversarial domain adaptation, thereby achieving efficient cross-domain fault diagnosis. Experimental results demonstrate that the proposed algorithm delivers effective fault diagnosis with minimal prior knowledge and outperforms comparable models. Furthermore, the DT system developed using the proposed model enhances the operational reliability of centrifugal pumps, reduces maintenance costs, and presents an innovative application of DT technology in industrial fault diagnosis.

Digital twin-enabled quality control through deep learning in industry 4.0: a framework for enhancing manufacturing performance

ABSTRACT In the context of Industry 4.0, integrating Digital Twin (DT) technology stands out as a critical challenge for enhancing manufacturing processes and productivity. The combination of DT and Artificial Intelligence (AI) provides a significant benefit for improving processes in real-time. Industries are actively researching these technologies to keep pace with the rapid evolution of technology, utilizing virtual representations for efficient real-time monitoring and control. The present paper proposes a new approach that relies on DT technology for monitoring, optimizing manufacturing processes and enabling quality control through Deep learning (DL). The proposed methodology involves creating a digital replica of the physical system and utilizing DL models for quality control purposes. This approach improves automation and productivity while maintaining high levels of quality assurance in factories. DL is deployed within the DT for gathering data from the physical system and making predictions regarding product quality. The approach is illustrated by considering an experimental industrial prototype. The results obtained are particularly intriguing, demonstrating heightened predictive accuracy in assessing product quality and real-time issue resolution. Overall, the findings underscore the significant and interesting impact of DT technology with DL on manufacturing processes in the context of Industry 4.0.

Fault analysis of oil-immersed transformer based on digital twin technology

Oil-immersed transformer is one of the important equipment to support the safe and stable operation of power system. At the same time, in the process of transformer operation, transformer failure is inevitable, this kind of failure belongs to multiple latent faults, which seriously endangers the safe operation of the transformer. Therefore, the maintenance of the transformer has played a crucial role. A fault detection method based on digital twin model is proposed to solve the problem that the internal dynamic characteristics of oil-immersed transformer are difficult to extract, and the fault detection error is large due to noise interference. The dynamic model of oil-immersed transformer is constructed, the main cause of failure is analysed, the failure time of internal insulator is taken as a linear index, and the linear correlation between failure probability and failure time is solved by Weibull distribution function. The fitting function between transformer fault residence time, operating days and fault probability variance is established, and the fitting value obtained is used as the initial data reference of the fault detection model. The digital twin model is used to establish the dynamic fault probability calculation function, and the on-site data is substituted to calculate the fault detection threshold, and then the data is compared to complete the detection.

Digital-Twin virtual model real-time construction via spatio-temporal cascade reconstruction for full-field plastic deformation monitoring in metal tube bending manufacturing

Digital Twin (DT) technology, which integrates multi-source information, is extensively applied for comprehensive monitoring, predicting, and optimizing manufacturing processes. The core of this technology is the Digital Twin Virtual Model (DTVM), which acts as a virtual mirror reflecting the real-world physical processes within a digital environment. In processes like tube bending, constructing a real-time DTVM capable of capturing full-field plastic deformation is essential for monitoring and analyzing plastic behavior. However, existing DTVMs often simplify spatial resolution and suffer from temporal delays, impeding the accurate real-time depiction of the complete state of the real physical processes. To address this issue, a real-time DTVM construction method based on spatio-temporal cascade reconstruction was proposed for full-field plastic deformation monitoring in metal tube bending. Initially, a joint-section driven predefined bending tube coordinate representation method was introduced to comprehensively capture the entire plastic deformation area in bending tubes. Subsequently, through a physics-derived model integrating limited real-time data and plastic forming theory, a low-fidelity model with complete but low accuracy was obtained. This model was subsequently refined into a high-fidelity model with both completeness and high accuracy using the proposed FPDR-Net. To eliminate temporal lags, the concept of compensation for time-delay through prediction was introduced. The newly developed TSCR-Net was applied to leverage past data to predict the present state, thereby achieving real-time synchronization mapping between the physical process and the DTVM. Finally, the proposed real-time reconstruction method for monitoring was validated through a case study on the bending of a 6061-T6 tube. The accuracy of full-field plastic deformation reconstruction was compared to traditional algorithms and finite element methods. The experimental results demonstrated that the proposed approach is highly efficient for real-time and full-field plastic deformation monitoring.

Enhancing Urban Sustainability and Resilience: Employing Digital Twin Technologies for Integrated WEFE Nexus Management to Achieve SDGs

This research explores the application of digital twin technologies to progress the United Nations’ Sustainable Development Goals (SDGs) within the water-energy-food-environment (WEFE) nexus management in urban refugee areas. The study in Irbid Camp utilizes a detailed 3D Revit model combined with real-time data and community insights processed through advanced machine learning algorithms. An examination of 450 qualitative interviews indicates an 80% knowledge level of water conservation practices among the community but only 35% satisfaction with the current management of resources. Predictive analytics forecast a 25% increase in water scarcity and an 18% surge in energy demand within the next ten years, prompting the deployment of sustainable solutions such as solar energy installations and enhanced rainwater collection systems. By simulating resource allocation and environmental impacts, the digital twin framework helps in planning urban development in line with SDGs 6 (Clean Water and Sanitation), 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), and 12 (Responsible Consumption and Production). This investigation highlights the capacity of digital twin technology to improve resource management, increase community resilience, and support sustainable urban growth, suggesting its wider implementation in comparable environments.

A digital twin-based assembly model for multi-source variation fusion on vision transformer

For the manufacture and assembly of the mechanical products, quality management is the key feature to improve the service performance, reduce the overall costs and enhance the sustainability of the manufacturing systems. While, with the emergence of advanced data-driven and digital twin technologies, the Zero-Defect Manufacturing (ZDM), which corrects, predicts, and prevents product defects based on multi-sources of data, is of great significance in improving assembly quality. As the specific application for the utilization of ZDM in product assembly, the critical performance index of the assembly results is predicted by taking into account the multi-source factors such as geometric deviation of the parts, material properties, assembly sequences and process boundary conditions during assembly. In this paper, we address the high computational cost and low computational efficiency of numerical simulation methods under multi-source factors, and propose a data-driven approach named DTA-VIT based on the fusion of heterogeneous variables for digital twin assembly modeling of products. Firstly, the geometric and performance variables of the assembly process are analyzed and modelled. Secondly, a multi-source assembly data fusion network under the Vision Transformer framework is developed. This network takes the parameter space, which fuses multi-source variables from the assembly process as input and the assembly result as output. Finally, a case study of the assembly process of composite bolted joint structures in aircraft assembly is conducted to verify the effectiveness and feasibility of the proposed method. The methodology provides a solid foundation for subsequent assembly quality control and prevent by predicting assembly performance efficiently, ultimately enabling the production of high-quality products.

Frequency Support Coordinated Control Strategy of Renewable Distributed Energy Resource Based on Digital Twins

The integration of high-penetration renewable energy sources is playing an increasingly important role in the frequency regulation of the power system. However, due to the varying output characteristics and response speeds of different renewable distributed energy resources (DERs), the overall response from the collective output of these distributed energy resources may not meet expected requirements and could have adverse effects on the grid. One drawback of traditional distributed coordinated control is its high communication requirements. This paper proposes using digital twin (DT) technology for the coordinated control of distributed energy resources, which can minimize the communication needs between various distributed energy resources while achieving coordinated control. Verification of the frequency support coordinated control strategy based on digital twin technology shows that it can effectively enhance the individual output characteristics and overall response of each distributed energy resource, providing effective support for grid frequency.

XDTEncoder: A Deep Explainable Arrhythmia Classification Framework for Smart Healthcare

In the context of smart healthcare, the integration of multimedia and digital twin technologies has driven significant advances in telemedicine. Electrocardiogram (ECG) signals, as a part of multimedia healthcare data, provide crucial digital information about the electrical activity of the heart, which is essential for diagnosing arrhythmias and ensuring a healthy life. Arrhythmia classification is a fundamental step in analyzing ECG signals and a critical problem for diagnosing heart diseases. One key challenge in arrhythmia classification is the lack of high accuracy for classifying arrhythmia heartbeats, and another key challenge is the lack of interpretability of decision-making models. This study aims to develop a novel approach to improve the performance of arrhythmia classification while providing explainable diagnostic decision paths. We propose XDTEncoder, an explainable arrhythmia classification framework that leverages multi-level features to classify arrhythmia heartbeats while offering an explainable diagnostic decision path. XDTEncoder is novel in three aspects. (1) It constructs a human-machine collaborative knowledge representation based on the Encoder-Decoder paradigm, which allows our model to classify arrhythmias while producing decision paths for cardiologists. (2) XDTEncoder compares two encoding methods (the binary tree encoding method and the Huffman encoding method) to embed the diagnostic decision tree into the arrhythmia classification framework. (3) XDTEncoder fuses multi-level features to improve the performance of arrhythmias classification. Evaluation on 5 types of arrhythmias in the MIT-BIH database demonstrates that our new approach outperforms state-of-the-art classifiers while providing interpretability.

Enhanced Medical Education for Physically Disabled People through Integration of IoT and Digital Twin Technologies

This research presents an innovative approach to revolutionize IoT service development in medical education, specifically designed to empower individuals with physical disabilities. By integrating digital twin technology, we offer dynamic virtual representations of tangible assets, facilitating real-time simulation, monitoring, and feedback. A unique visual response algorithm has been developed to enhance the processing of visual vector data, resulting in a more efficient IoT service development process. Our method demonstrates superior performance over traditional techniques, particularly in achieving higher intrinsic variable merging values, which is critical for accurate and accessible visualization. The practical applications of this technology are highlighted through case studies that demonstrate how physically disabled students can benefit from interactive and immersive educational experiences. For instance, students can engage with the digital twins of medical equipment, allowing them to practice procedures and gain hands-on experience in a virtual environment without physical barriers. This approach not only improves accessibility but also personalizes learning experiences, adapting to the unique needs of each student. The research underscores the importance of inclusive design in developing IoT services, ensuring higher inclusivity rates and addressing diverse learning patterns. The findings suggest that the integration of IoT and digital twin technologies can significantly enhance medical education, making it more accessible, effective, and inclusive for physically disabled individuals. This study lays the groundwork for future advancements in this field, highlighting the potential for ongoing technological innovations to further transform medical education.

Digital twin for smart metro service platform: Evaluating long-term tunnel structural performance

The metro system is crucial for urban transportation networks because it significantly improves traffic flow, with tunnels playing a key role in ensuring the safety and quality of metro services. Over time, the structural performance of metro tunnels deteriorates, posing risks to the system's operational dependability. This paper highlights the importance of accurately assessing the long-term structural performance of metro tunnels for operations and maintenance (O&M) decision-making. Digital twin (DT) technology is introduced as the core of the O&M management platform for metro tunnels. This paper presents the structure of a metro tunnel DT and designs a smart metro service platform (SMSP) based on the DT to enhance long-term structural performance through data collection, early warning, and maintenance management. The deployment of SMSP in a metro project and its effect on the long-term evolution of tunnel structural performance are examined.

State of the art and future directions of digital twin-enabled smart assembly automation in discrete manufacturing industries

ABSTRACT Digital twin (DT) technology is increasingly recognised for its potential to transform the manufacturing industry, particularly in assembly automation. By offering real-time insights and optimisation capabilities, DTs enhance efficiency, reduce costs, and improve product quality. This paper critically reviews DT-enabled smart factories in assembly automation, aiming to evaluate the current state, identify common practices, and propose a taxonomy for standardisation. The taxonomy outlines key factors in DT-enabled systems, including data sources, visualisation, applications, functional outputs, digital coupling, and IoT protocols. These classifiers help navigate the complex frameworks of DTs, guiding the design and implementation of assembly automation systems. Through a comprehensive analysis of 60 key publications, the review categorises DT objectives, propositions, and implementation technologies, identifying common themes and addressing gaps in current research. The findings emphasise the importance of integrating DTs with existing systems, ensuring reliability and security in data-driven DTs, and pursuing sustainable solutions beyond energy-intensive industries. This review not only informs future research but also provides a framework to guide industry practices in the development of DT-enabled assembly automation systems.

Optimization of structural reinforcement assessment for architectural heritage digital twins based on LiDAR and multi-source remote sensing

This study investigates the geometric modelling of architectural heritage digital twins constructed based on multi-source point cloud data and its effectiveness in structural reinforcement assessment. Particular emphasis has been placed on the use of static stiffness rules to identify areas of structural weakness in the geometric models of digital twins and the need for their reinforcement, in order to prevent potential structural problems and to ensure the long-term preservation of the built heritage. Taking Yingxian wooden pagoda as a study case, based on the collection of multi-source point cloud data, the digital twin geometric model is constructed through fine modelling, decoupling of digital models, and geometric transformation. This enhances the true reflection of the column-architrave structure morphology, providing a more accurate model for structural stress analysis. Based on verifying the accuracy of the digital twin geometric model, the instability conditions are identified through static stiffness rules and the deformation values at multiple points are analyzed, enabling precise identification of weak areas in the column-architrave structure. Two types of reinforcement measures are designed and simulated for the structural weak areas identified through the geometric modelling, and the optimal reinforcement scheme is obtained after detailed analysis, according to which specific adjustments and optimization strategies are proposed to enhance the overall stability and durability of the structure. The results showed that the maximum deformation value of 4.65 mm existed in column M2W23, which required reinforcement. Aluminum reinforcement reduced the deformation to 3.5 mm (24.7% reduction), while CFRP fabric reinforcement was more effective, reducing the deformation to 2.8 mm (39.7% reduction), showing high stability. The research results demonstrate the potential application of digital twin technology in architectural heritage preservation and restoration, providing methodological and empirical guidance for heritage preservation research.

HeXEHRS: FHIR-Based Cloud EHR Services to Support Healthcare in Depopulated Areas Driven by Digital Twin.

HeXEHRS is a FHIR-based cloud EHR service designed to support healthcare in depopulated areas, powered by digital twin technology. Its core functionalities encompass standard EHR tasks including data exchange for healthcare processes. In the first year of this national project, we present the design and define the functionalities of the system.

Reference Information Model and Interoperable Architecture for Digital Health Twins: A Position Paper.

Digital Health Twins (DHTs) hold immense potential to revolutionize healthcare by providing personalized virtual models of individual patients and their health conditions. This position paper discusses about the needs to overcoming the challenges in standardization and information models for data integration, collection, and visualization for realizing the full potential with digital twin technology. By addressing these challenges, digital health twins can enable better decision-making, enhance patient care, and contribute to the advancement of personalized healthcare solutions.

Exploring the barriers to digital twin adoption in the Nigerian construction industry: a structural equation modelling approach

Purpose The adoption of emerging technologies is critical to enhance construction industry performance. Previous studies have shown that the Nigerian construction industry (NCI) is slow to adopt digital technologies and faces performance issues. As a result, this study aims to investigate and model the barriers to adopting digital twin (DT) technology in the NCI with the view to provide stakeholders with adequate information on the multifaceted nature of DT barriers and provide strategies to improve DT adoption. Design/methodology/approach The study adopts a qualitative and quantitative approach to achieve the overall aim of the study. The qualitative approach included a scoping review used to identify barriers to DT adoption from the literature. A five-point Likert scale questionnaire was administered to 246 construction professionals in the NCI. This was followed by critical analysis using mean ranking and standard deviation, Kruskal–Wallis, factor analysis and partial least squares structural equation modelling (PLS-SEM). Findings The exploratory factor analysis revealed four categories of barriers to DT adoption in the NCI: “technological and investment”, “data management and government”, “project and human resources” and “digital transformation”. The PLS-SEM results revealed the causal relationships of four barriers categories and their concomitant effects on DT adoption in the NCI. The top three barrier categories that require critical attention in order of significance are: technological and investment (ß = 0.655), data management and government (ß = 0.313) and project and human resources (ß = 0.194). Digital transformation (ß = −0.046) has the least significance. Overall, all the barriers’ categories were accepted at a significance level of p < 0.05. Practical implications The practical implications include guiding policymakers and practitioners in making informed decisions to address the identified barriers to DT adoption in the NCI. The findings may also be applicable to other developing countries in Africa and beyond. By implementing effective policies and stakeholder guidelines, the NCI can advance technologically and enhance its competitiveness to execute advanced construction projects. Originality/value This research contributes to the construction industry by shedding light on the barriers to DT adoption and their intricate interconnections within the NCI context. It is also the first study in NCI context to present the level of DT awareness and explore the concomitant effects of the barriers.