Digital Twin Framework Research Articles

Data-driven modeling in digital twin for power system anomaly detection

Background: Power system anomaly detection is of great significance for realizing system situation awareness and early detection of system operating risks. In view of the complex operating conditions of the system, there are a large number of opaque links in the mechanism, and the anomaly detection approach based on physical mechanism modeling is prone to system errors due to assumptions, simplification, and transfer in the modeling process. This paper focuses on digital twin based data-driven approaches for power system anomaly detection to compensate for the limitation of physical methods in dynamical modeling. Methods: First of all, a digital twin framework for power system real-time analysis is constructed based on the concept of digital twin. Then, this paper conducts researches on the core of the designed framework, i.e., digital twin modeling. Considering the complexity of power system operating conditions, data-driven modeling is preferred and a random matrix and free probability theory based model for anomaly detection of system operating situation is constructed. Results: Simulation data with different spatiotemporal structure generated through a Monte Carlo experiment verified the sensitivity of the constructed model for data correlations. Meanwhile, the case on the system operating data generated through the IEEE 118-bus system validate the effectiveness of the proposed model for the system anomaly detection. Conclusions: The constructed data-driven model can accurately characterize the correlations among data elements, has good sensitivity to the variation of data spatial and temporal correlations, and can depict the data residuals better than the M-P law curve, which indicates the practicability and necessity of the constructed data-driven model for the digital twin modeling of power system anomaly detection.

IoT-based framework for digital twins in steel production: A case study of key parameter prediction and optimization for CSR

Cold-rolled strips are a pivotal product of advanced strip rolling technology in iron and steel production with stringent process standards. Over an extended period, the existence of 'information silos' between cold strip rolling (CSR) and its upstream processes, particularly hot strip rolling (HSR), causes a persistent challenge in achieving collaborative optimization throughout the entire process, thereby constraining progress in product quality. To overcome this challenge, a digital twin framework was proposed to consolidate diverse data from HSR and CSR production lines into a centralized data centre, forming a 'data continent.' This approach establishes a comprehensive dataset for model-driven training and prediction in virtual space. An improved Light gradient boosting machine (IlightGBM) is introduced, enabling high-precision rolling force prediction in unsteady-state rolling and providing essential variables for establishing the tension optimization objective function. A control function is implemented to minimize rolling force fluctuations, and three population algorithms (TPA) are devised for its solution. This strategy successfully realizes collaborative tension optimization across multiple stands, with the rolling speed acting as a follower. The proposed approach was implemented in a large-scale steel plant equipped with a complete HSR and CSR production line, effectively mitigating flatness defects and thickness deviation in the strip's head section. Thus, the proposed framework is reliable and can be flexibly applied to strip manufacturing processes.

Digital Twin-Based Cyber-Attack Detection Framework for Cyber-Physical Manufacturing Systems

Smart manufacturing (SM) systems utilize run-time data to improve productivity via intelligent decision-making and analysis mechanisms on both machine and system levels. The increased adoption of cyber-physical systems in SM leads to the comprehensive framework of cyber-physical manufacturing systems (CPMS) where data-enabled decision-making mechanisms are coupled with cyber-physical resources on the plant floor. Due to their cyber-physical nature, CPMS are susceptible to cyber-attacks that may cause harm to the manufacturing system, products, or even the human workers involved in this context. Therefore, detecting cyber-attacks efficiently and timely is a crucial step toward implementing and securing high-performance CPMS in practice. This paper addresses two key challenges to CPMS cyber-attack detection. The first challenge is distinguishing expected anomalies in the system from cyber-attacks. The second challenge is the identification of cyber-attacks during the transient response of CPMS due to closed-loop controllers. Digital twin (DT) technology emerges as a promising solution for providing additional insights into the physical process (twin) by leveraging run-time data, models, and analytics. In this work, we propose a DT framework for detecting cyber-attacks in CPMS during controlled transient behavior as well as expected anomalies of the physical process. We present a DT framework and provide details on structuring the architecture to support cyber-attack detection. Additionally, we present an experimental case study on off-the-shelf 3D printers to detect cyber-attacks utilizing the proposed DT framework to illustrate the effectiveness of our proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by developing a general-purpose and extensible digital twin-enabled cyber-attack detection framework for manufacturing systems. Existing works in the field consider specialized attack scenarios and models that may not be extensible in practical manufacturing scenarios. We utilize digital twin (DT) technology as a key enabler to develop a systematic and extensible framework where we identify the abnormality of a resource and detect if the abnormality is due to an attack or an expected anomaly. We provide several remarks on how our proposed framework can extend existing industrial control systems (ICS) and can accommodate further extensions. The presented DTs utilize data-driven machine learning models, physics-based models, and subject matter expert knowledge to perform detection and differentiation tasks in the context of expected anomalies and model-based controllers that control the manufacturing process between multiple setpoints. We utilize a model predictive controller on an off-the-shelf 3D printer to run the process, and stage anomalies and cyber-attacks that are successfully detected by the proposed framework.

Multi-domain data integration and management for enhancing service-oriented digital twin for infrastructure operation and maintenance

The evolution from Building Information Modelling (BIM) to Digital Twins (DT) marks a shift from static models to dynamic data-centric representations. Using DT can improve Infrastructure Operation and Maintenance (IOM) through prompt and comprehensive data, especially by providing services and value-added features. However, developing this service-oriented digital twin (SoDT) for IOM faces challenges due to data heterogeneity, fragmentation, and interoperability issues across various systems used by different asset authorities. This paper develops a service-oriented digital twin framework for IOM (SoDT-IOM), which focuses on multi-domain data integration and IOM service-oriented design. Service-oriented DT components and data integration mechanisms are developed, and an individual-multiple interacted data repository for IOM services is designed. Two-stage industrial interviews are conducted, and two use cases are illustrated to evaluate the functionality and efficacy of the framework. The evaluation results reveal that implementing a SoDT-IOM framework encounters challenges related to cost and data security; however, it is appreciated for its capacity to improve decision-making and collaboration with linked, accurate and timely data. This paper proposes further design requirements and implementation recommendations for SoDT of asset management from both technical and managerial perspectives.

Smart landscaping design for sustainable net-zero energy smart cities: Modeling energy hub in digital twin

In our study, we introduce an innovative energy management system optimized for energy hubs in net-zero energy smart cities, emphasizing sustainability as a core objective. Our approach intricately integrates distributed generation systems and storage facilities within a digital twin framework, incorporating landscape considerations to promote environmental harmony. By prioritizing sustainability, our strategy aims to minimize operational costs while maximizing energy efficiency and reducing carbon emissions. We employ specialized energy flow algorithms for each grid, designing energy hubs that leverage renewable resources and mobile storage to enhance sustainability. To address complexity, our model utilizes a non-convex mixed-integer nonlinear programming structure and robust adaptive optimization techniques, combining the Gray Wolf and Krill Herd algorithms. Through simulations, we demonstrate significant improvements in power system efficiency, including notable reductions in power loss, peak voltage fluctuations, and temperature variations. Our research underscores the critical role of smart landscaping in advancing sustainable energy management for net-zero energy smart cities, facilitating seamless integration of energy hubs with the surrounding landscape within a digital twin paradigm.

Enhancing Coastal Risk Recognition: Assessing UAVs for Monitoring Accuracy and Implementation in a Digital Twin Framework

This paper addresses the intricate challenges of coastal management, particularly in rapidly forming tidal flats, emphasizing the need for innovative monitoring strategies. The dynamic coastal topography, exemplified by a newly formed tidal flat in Shanghai, underscores the urgency of advancements in coastal risk recognition. By utilizing a digital twin framework integrated with state-of-the-art unmanned aerial vehicles (UAVs), we systematically evaluate three configurations and identify the optimal setup incorporating real-time kinematics (RTK) and light detection and ranging (LiDAR). This UAV configuration excels in efficiently mapping the 3D coastal terrain. It has an error of less than 0.1 m when mapping mudflats at an altitude of 100 m. The integration of UAV data with a precise numerical ocean model forms the foundation of our dynamic risk assessment framework. The results showcase the transformative potential of the digital twin framework, providing unparalleled accuracy and efficiency in coastal risk recognition. Visualization through Unity Engine or Unreal Engine enhances accessibility, fostering community engagement and awareness. By predicting and simulating potential risks in real-time, this study offers a forward-thinking strategy for mitigating coastal dangers. This research not only contributes a comprehensive strategy for coastal risk management but also sets a precedent for the integration of cutting-edge technologies in safeguarding coastal ecosystems. The findings are significant in paving the way for a more resilient and sustainable approach to coastal management, addressing the evolving environmental pressures on our coastlines.

Personalized Diabetes Management with Digital Twins: A Patient-Centric Knowledge Graph Approach.

Diabetes management requires constant monitoring and individualized adjustments. This study proposes a novel approach that leverages digital twins and personal health knowledge graphs (PHKGs) to revolutionize diabetes care. Our key contribution lies in developing a real-time, patient-centric digital twin framework built on PHKGs. This framework integrates data from diverse sources, adhering to HL7 standards and enabling seamless information access and exchange while ensuring high levels of accuracy in data representation and health insights. PHKGs offer a flexible and efficient format that supports various applications. As new knowledge about the patient becomes available, the PHKG can be easily extended to incorporate it, enhancing the precision and accuracy of the care provided. This dynamic approach fosters continuous improvement and facilitates the development of new applications. As a proof of concept, we have demonstrated the versatility of our digital twins by applying it to different use cases in diabetes management. These include predicting glucose levels, optimizing insulin dosage, providing personalized lifestyle recommendations, and visualizing health data. By enabling real-time, patient-specific care, this research paves the way for more precise and personalized healthcare interventions, potentially improving long-term diabetes management outcomes.

A Privacy Preserving Federated Learning (PPFL) Based Cognitive Digital Twin (CDT) Framework for Smart Cities

A Smart City is one that makes better use of city data to make our communities better places to live. Typically, this has 3 components: sensing (data collection), analysis and actuation. Privacy, particularly as it relates to citizen's data, is a cross-cutting theme. A Digital Twin (DT) is a virtual replica of a real-world physical entity. Cognitive Digital Twins (CDT) are DTs enhanced with cognitive AI capabilities. Both DTs and CDTs have seen adoption in the manufacturing and industrial sectors however cities are slow to adopt these because of privacy concerns. This work attempts to address these concerns by proposing a Privacy Preserving Federated Learning (PPFL) based Cognitive Digital Twin framework for Smart Cities.

A digital twin framework for innovating rural ecological landscape control

BackgroundBecause cities prioritize economic development and face ecological space and resource constraints, the development of rural areas, which have untapped potential, should receive increased attention. Consequently, rural ecological landscapes should be constructed through the control of land use types and quality to fully ensure the sustainable development of urban and rural ecosystems. The digital twin is a philosophy and a methodology that connects the digital and physical realms, facilitating realistic and dynamic mapping simulations of the real world. This capability offers valuable insights for digital decision-making, maintenance, and optimization of rural ecological landscapes. Given the digital transformation of rural ecological landscape control, this paper proposes a rural intelligent control approach based on the digital twin concept and new technology.MethodsFive components of the rural ecological landscape digital twin framework are selected to collectively facilitate the monitoring and analysis of rural conditions, formulate strategic solutions, implement management and control behaviors, and enhance participant interaction. The method includes three steps: mapping and fusing information, constructing and managing a database, and constructing a digital platform. Data mining and spatial fusion are performed through targeted mapping methods, and Oracle and ArcGIS SDE are utilized for database construction and fused data management. The twin platform is generated via HTML, desktop application development and geographic information system development technologies using a distributed system as the core.ResultsBased on multiple case studies, our platform efficiently gathers system information on rural ecological landscapes using a twin model. Through evaluation and analysis, it determines landscape governance zones, adjusting them based on land use conditions. The platform refines control schemes with feedback from diverse users, ensuring effective control in various scenarios. Its key advantages include high development efficiency, flexible access, and smooth cross-platform integration. Although implemented in rural China, the proposed digital twin framework is applicable to any rural area requiring ecological landscape digital control.ConclusionsThe value of the platform lies in its powerful information processing capability, overcoming the limitations of time and space and enabling the presentation and integration of fictional scenes Thus, the platform provides a reference for the digital transformation of rural ecological landscape control.

Digital twinning of temperature fields for modular multilayer multiphase pipeline structures

The temperature field of oil and gas wells in the field of petroleum engineering presents a core problem and challenge in the digital twin framework due to its ultra-long-distance and highly variable structural characteristics. The varying wellbore cross-sectional structures with depth make it difficult to establish an effective and generalized analytical model for heat transfer. In this study, we propose, for the first time, a method to automate the construction of multi-layered and multi-component heat transfer models by using a general computational model based on non-steady-state single-phase structural modules. This method enables the automated generation of complex multi-layered and multi-component heat transfer models, thereby achieving the construction of a generalized model for temperature field characterization with varying wellbore cross-sectional structures over ultra-long distances. Utilizing this modeling approach, we validate the proposed method through case studies using actual wellbore temperature field data. The results demonstrate the lightweight and efficient computational analysis of temperature field information under non-steady-state conditions.

A Hybrid-Model-Based CNC Machining Trajectory Error Prediction and Compensation Method

Intelligent manufacturing is the main direction of Industry 4.0, pointing towards the future development of manufacturing. The core component of intelligent manufacturing is the computer numerical control (CNC) system. Predicting and compensating for machining trajectory errors by controlling the CNC system’s accuracy is of great significance in enhancing the efficiency, quality, and flexibility of intelligent manufacturing. Traditional machining trajectory error prediction and compensation methods make it challenging to consider the uncertainties that occur during the machining process, and they cannot meet the requirements of intelligent manufacturing with respect to the complexity and accuracy of process parameter optimization. In this paper, we propose a hybrid-model-based machining trajectory error prediction and compensation method to address these issues. Firstly, a digital twin framework for the CNC system, based on a hybrid model, was constructed. The machining trajectory error prediction and compensation mechanisms were then analyzed, and an artificial intelligence (AI) algorithm was used to predict the machining trajectory error. This error was then compensated for via the adaptive compensation method. Finally, the feasibility and effectiveness of the method were verified through specific experiments, and a realization case for this digital-twin-driven machining trajectory error prediction and compensation method was provided.

Augmenting cybersecurity in smart urban energy systems through IoT and blockchain technology within the Digital Twin framework

This research presents a comprehensive framework to enhance the cybersecurity and efficiency of smart urban energy systems by integrating cutting-edge technologies. Leveraging advanced evolutionary optimization techniques, specifically Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the proposed system optimizes energy management and trading within urban infrastructure. Simultaneously, the integration of Internet of Things (IoT) devices enables real-time monitoring and decision-making, while blockchain technology ensures secure and transparent transactions. The study evaluates system performance by comparing it with traditional approaches, assessing key metrics such as convergence speed, exploration, exploitation, and cybersecurity. Results indicate that the proposed framework outperforms traditional systems in energy efficiency, cost-effectiveness, and cybersecurity metrics. This research offers valuable insights into integrating emerging technologies to address evolving challenges in smart urban energy systems. The findings highlight the potential of this innovative framework to revolutionize urban energy management, providing a foundation for more resilient, secure, and efficient smart cities. As the global urban landscape continues to evolve, this framework represents a strategic approach to meet the increasing demands for sustainable and secure urban energy solutions.

Application Scenarios of Digital Twins for Smart Crop Farming through Cloud–Fog–Edge Infrastructure

In the last decade, digital twin (DT) technology has received considerable attention across various domains, such as manufacturing, smart healthcare, and smart cities. The digital twin represents a digital representation of a physical entity, object, system, or process. Although it is relatively new in the agricultural domain, it has gained increasing attention recently. Recent reviews of DTs show that this technology has the potential to revolutionise agriculture management and activities. It can also provide numerous benefits to all agricultural stakeholders, including farmers, agronomists, researchers, and others, in terms of making decisions on various agricultural processes. In smart crop farming, DTs help simulate various farming tasks like irrigation, fertilisation, nutrient management, and pest control, as well as access real-time data and guide farmers through ‘what-if’ scenarios. By utilising the latest technologies, such as cloud–fog–edge computing, multi-agent systems, and the semantic web, farmers can access real-time data and analytics. This enables them to make accurate decisions about optimising their processes and improving efficiency. This paper presents a proposed architectural framework for DTs, exploring various potential application scenarios that integrate this architecture. It also analyses the benefits and challenges of implementing this technology in agricultural environments. Additionally, we investigate how cloud–fog–edge computing contributes to developing decentralised, real-time systems essential for effective management and monitoring in agriculture.

Digital twin framework for smart greenhouse management using next-gen mobile networks and machine learning

Due to the increase in world population, arable land has been reduced. Consequently, the concept of urban greenhouses is on the rise. Smart greenhouses need to monitor physical parameters for the healthy growth of plants from remote locations. A digital twin is a representation of physical assets in the digital world, and this emerging technology has opened up opportunities for efficient system development for Industry 4.0. The digital twin receives real-time operational data to monitor the asset in the digital domain. It performs real-time processing, data analysis, and machine learning to predict optimized decisions. In the era of next-generation mobile networks, IoT devices can communicate and perform their remote operations in a timely manner. In smart greenhouse technology, the digital twin could be a revolutionary substitute for real-time remote monitoring and process management. However, there has been limited work on digital twin-driven smart greenhouse technology. In this paper, a process management framework is developed that can be interpreted as a machine learning and cloud-based data-driven digital twin for smart greenhouses. The proposed framework consists of three layers: the physical, fog, and cloud layers. The physical greenhouse measurements are monitored using a highly immersive cloud-based, real-time 3D environment. We present an example architecture using commercial cloud and open-source tools to verify the proof of concept. Additionally, different ML techniques are utilized to predict the operational requirements for smart greenhouses.

Bidirectional graphics-based digital twin framework for quantifying seismic damage of structures using deep learning networks

Tremendous effort has been devoted toward developing automated post-earthquake inspection techniques, including automated image collection and damage identification. However, few studies have attempted to establish the complex relationship between visible damage and structural conditions. Moreover, the lack of training data further hinders the potential use of deep learning algorithms. This paper proposes a framework, termed Bidirectional Graphics-based Digital Twin (Bi-GBDT), that allows for assessment of the structural condition based on post-earthquake photographic surveys. The procedure contains two parts: (i) A GBDT of a target structure, containing a computer graphics model and a finite element (FE) model, is developed (ii) Synthetic data subsequently is generated from the GBDT to train neural networks to predict the damage measures and structural conditions. To demonstrate the proposed approach, a seismically-designed reinforced concrete shear wall is considered. First, the GBDT of the shear wall is constructed and calibrated so that the associated damage patterns simulated by the corresponding FE model match well with the experimental results. Next, synthetic images of the damage patterns are created from the validated GBDT, along with the corresponding structural damage measures, and used to train Residual Neural Network and Conditional Generative Adversarial Networks to determine the maximum drift, the stress/strain fields and the structural condition. The neural networks trained on synthetic data are shown to perform well for the experimental data, confirming the proposed approach. Subsequently, the neural networks are tested on the synthetic data for a wide variety of loading conditions to demonstrate the robustness of the approach. In addition, the realistic images rendered from the GBDT are utilized as input to predict the structural condition, showcasing the comprehensive Bi-GBDT framework. These results demonstrate the efficacy of the proposed approach to generate accurate digital twins and point toward its future application for development of automated post-earthquake assessment strategies.

Research on the development and application of digital twin for the full life cycle of bridge engineering

Bridges play an important role in modern society, whereas many bridges are in poor condition and are characterized by complexity, randomness, and risk no matter what phase the bridges are in. Meanwhile, with advanced technologies such as IoT and Big Data, Digital Twin has been developed in different industries, such as aerospace. With multiple superiorities of real-time data detection, big data processing, full life-cycle management, etc., it is appropriate for bridge engineering. The paper aims to present a state-of-the-art literature review on the full life cycle of the application of Digital Twin in Bridge Engineering. The article discusses the relationship between Digital Twin and Building Information Model (BIM) and constructs a comprehensive framework for Digital Twin in Bridge Engineering. The review is classified into three main parts design and construction phase, operation and maintenance phase, and demolition phase based on different stages of the full life cycle. The results show the applications high potential and feasibility, and show that most of the current research focuses on the operation and maintenance phases. However, intensive research is still necessary to improve the accuracy, quality, efficiency, timeliness, and versatility of the Digital Twin in Bridge Engineering.

Integrating FMI and ML/AI models on the open‐source digital twin framework OpenTwins

AbstractThe realm of digital twins is experiencing rapid growth and presents a wealth of opportunities for Industry 4.0. In conjunction with traditional simulation methods, digital twins offer a diverse range of possibilities. However, many existing tools in the domain of open‐source digital twins concentrate on specific use cases and do not provide a versatile framework. In contrast, the open‐source digital twin framework, OpenTwins, aims to provide a versatile framework that can be applied to a wide range of digital twin applications. In this article, we introduce a re‐definition of the original OpenTwins platform that enables the management of custom simulation services and the management of FMI simulation services, which is one of the most widely used simulation standards in the industry and its coexistence with machine learning models, which enables the definition of the next‐gen digital twins. Thanks to this integration, digital twins that reflect reality better can be developed, through hybrid models, where simulation data can feed the scarcity of machine learning data and so forth. As part of this project, a simulation model developed through the hydraulic software Epanet was validated in OpenTwins, in addition to an FMI simulation service. The hydraulic model was implemented and tested in an agricultural use case in collaboration with the University of Córdoba, Spain. A machine learning model has been developed to assess the behavior of an FMI simulation through machine learning.

Trends in Digital Twin Framework Architectures for Smart Cities: A Case Study in Smart Mobility.

The main aim of this paper is to present an innovative approach to addressing the challenges of smart mobility exploiting digital twins within the METACITIES initiative. We have worked on this issue due to the increasing complexity of urban transportation systems, coupled with the urgent need to improve efficiency, safety, and sustainability in cities. The work presented in this paper is part of the project METACITIES, an Excellence Hub that spans a large geographical area, that of Southeastern Europe. The approach of the Greek innovation ecosystem of METACITIES involves leveraging digital twin technology to create intelligent replicas of urban mobility environments, enabling real-time monitoring, analysis, and decision making. Through use cases such as "Smart Parking", "Environmental Behavior Analysis on Traffic Incidents", and "Emergency Management", we demonstrate how digital twins can optimize traffic flow, mitigate environmental impact, and enhance emergency response; these use cases will be tested on a small scale, before deciding on implementation at a larger and more expensive scale. The final outcome is the METACITIES Architecture for smart mobility, which will be part of an Open Digital Twin Framework capable of evolving a smart city into a metacity.

Towards efficient traffic crash detection based on macro and micro data fusion on expressways: A digital twin framework

AbstractEfficient detection of traffic crashes has been a significant matter of concern with regards to expressway safety management. The current challenge is that, despite collecting vast amounts of data, expressway detection equipment is plagued by low data utilization rates, unreliable crash detection models, and inadequate real‐time updating capabilities. This study is to develop an effective digital twin framework for the detection of traffic crashes on expressways. Firstly, the digital twin technology is used to create a virtual entity of the real expressway. A fusion method for macro and micro traffic data is proposed based on the location of multi‐source detectors on a digital twin platform. Then, a traffic crash detection model is developed using the ThunderGBM algorithm and interpreted by the SHAP method. Furthermore, a distributed strategy for detecting traffic crashes is suggested, where various models are employed concurrently on the digital twin platform to enhance the general detection ability and reliability of the models. Finally, the efficacy of the digital twin framework is confirmed through a case study of certain sections of the Nanjing Ring expressway. This study is expected to lay the groundwork for expressway digital twin studies and offer technical assistance for expressway traffic management.

AIoT-enabled digital twin system for smart tunnel fire safety management

High traffic flow in a confined tunnel makes fire safety a critical issue. This paper proposed a digital twin framework for tunnel fire safety management in real-time, driven by dynamic sensor data and AIoT technologies. A deep learning model trained by the Transformer network and simulation dataset is used to predict real-time fire location and size. Then, the AI model is integrated into a 3D digital twin platform developed by the game engine Unity 3D. The performance of the proposed digital twin framework is demonstrated using numerical experiments and large-scale tunnel fire tests. Results show that the established AI model achieved promising accuracy in predicting fire location and power for both numerical and experimental data. The digital twin platform can also visualize the 3D fire scene that supports evacuation, firefighting, and emergency rescue. This research demonstrates the feasibility of using a 3D environment and digital twin in real-time fire safety management.