Digital Twin Networks: A Survey Wu, Yiwen; Zhang, Ke; Zhang, Yan
IEEE internet of things journal,
09/2021, Letnik:
8, Številka:
18
Journal Article
Digital twin network (DTN) is an emerging network that utilizes digital twin (DT) technology to create the virtual twins of physical objects. DTN realizes co-evolution between physical and virtual ...spaces through DT modeling, communication, computing, data processing technologies. In this article, we present a comprehensive survey of DTN to explore the potentiality of DT. First, we elaborate key features and definitions of DTN. Next, the key technologies and the technical challenges in DTN are discussed. Furthermore, we depict the typical application scenarios, such as manufacturing, aviation, healthcare, 6G networks, intelligent transportation systems, and urban intelligence in smart cities. Finally, the new trends and open research issues related to DTN are pointed out.
Digital twin modeling Tao, Fei; Xiao, Bin; Qi, Qinglin ...
Journal of manufacturing systems,
July 2022, 2022-07-00, Letnik:
64
Journal Article
Recenzirano
The digital twin is an emerging and vital technology for digital transformation and intelligent upgrade. Driven by data and model, the digital twin can perform monitoring, simulation, prediction, ...optimization, and so on. Specifically, the digital twin modeling is the core for accurate portrayal of the physical entity, which enables the digital twin to deliver the functional services and satisfy the application requirements. Therefore, this paper provides systematic research of current studies on the digital twin modeling. Since the digital twin model is a faithful reflection of the digital twin modeling performance, a comprehensive and insightful analysis of digital twin models is given first from the perspective of the application field, hierarchy, discipline, dimension, universality, and functionality. Based on the analysis of digital twin models, current studies on the digital twin modeling are classified and analyzed according to the six modeling aspects within the digital twin modeling theoretical system proposed in our previous work. Meanwhile, enabling technologies and tools for the digital twin modeling are investigated and summarized. Finally, observations and future research recommendations are presented.
•Provide a systematic analysis of currently available digital twin models from multiple perspectives.•Give a complete summary of the whole digital twin modeling process from multiple aspects.•Research the enabling technologies and tools applied in each aspect of the digital twin modeling.•Recommend future research orientations of the digital twin model and modeling.•Propose prospective approaches to resolve the existing problems and emerging challenges.
With the developments and applications of the new information technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, a smart manufacturing era is coming. At ...the same time, various national manufacturing development strategies have been put forward, such as Industry 4.0 , Industrial Internet , manufacturing based on Cyber-Physical System , and Made in China 2025 . However, one of specific challenges to achieve smart manufacturing with these strategies is how to converge the manufacturing physical world and the virtual world, so as to realize a series of smart operations in the manufacturing process, including smart interconnection, smart interaction, smart control and management, etc. In this context, as a basic unit of manufacturing, shop-floor is required to reach the interaction and convergence between physical and virtual spaces, which is not only the imperative demand of smart manufacturing, but also the evolving trend of itself. Accordingly, a novel concept of digital twin shop-floor (DTS) based on digital twin is explored and its four key components are discussed, including physical shop-floor, virtual shop-floor, shop-floor service system, and shop-floor digital twin data. What is more, the operation mechanisms and implementing methods for DTS are studied and key technologies as well as challenges ahead are investigated, respectively.
•The origin and evolution of Digital Twin (DT) are reviewed, along with its implementation process. To describe the physical shop-floor more comprehensively and accurately, a five-dimensional ...modeling approach of Shop-floor Digital Twin (SDT), including geometry, physics, behavior, rule, and data, is proposed. ESHLEP-N model is to build the operation logic of shop-floor. Markov chain is used in the modeling of deduction rules. A shop-floor data management model is constructed to provide support for modeling in other dimensions.•Aiming at elements and processes on the physical shop-floor, a DT-based 3D visual and real-time monitoring approach and a Markov chain-based prediction approach are proposed, for monitoring and predicting shop-floor operating status based on the constructed SDT.•A DT-based visual monitoring and prediction system for shop-floor operating status, called DT-VMPS, is developed. The engineering practicability of the proposed technique is verified with a case study, which promotes the application of DT in the production stage.
Digital twin (DT) technology provides a novel, feasible, and clear implementation path for the realization of smart manufacturing and cyber-physical systems (CPS). Currently, DT is applied to all stages of the product lifecycle, including design, production, and service, although its application in the production stage is not yet extensive. Shop-floor digital twin (SDT) is a digital mapping model of the corresponding physical shop-floor. How to build and apply SDT has always been challenging when applying DT technology in the production phase. To address the existing problems, this paper first reviews the origin and evolution of DT, including its application status in the production stage. Then, an implementation framework for the construction and application of SDT is proposed. Three key implementation techniques are explained in detail: the five-dimensional modeling of SDT; DT-based 3D visual and real-time monitoring of shop-floor operating status; and prediction of shop-floor operating status based on SDT using Markov chain. A DT-based visual monitoring and prediction system (DT-VMPS) for shop-floor operating status is developed, and the feasibility and effectiveness of the proposed method are demonstrated through the use of an engineering case study. Finally, a summary of the contributions of the paper is given, and future research issues are discussed.
Digital twin is often viewed as a technology that can assist engineers and researchers make data-driven system and network-level decisions. Across the scientific literature, digital twins have been ...consistently theorized as a strong solution to facilitate proactive discovery of system failures, system and network efficiency improvement, system and network operation optimization, among others. With their strong affinity to the industrial metaverse concept, digital twins have the potential to offer high-value propositions that are unique to the energy sector stakeholders to realize the true potential of physical and digital convergence and pertinent sustainability goals. Although the technology has been known for a long time in theory, its practical real-world applications have been so far limited, nevertheless with tremendous growth projections. In the energy sector, there have been theoretical and lab-level experimental analysis of digital twins but few of those experiments resulted in real-world deployments. There may be many contributing factors to any friction associated with real-world scalable deployment in the energy sector such as cost, regulatory, and compliance requirements, and measurable and comparable methods to evaluate performance and return on investment. Those factors can be potentially addressed if the digital twin applications are built on the foundations of a scalable and interoperable framework that can drive a digital twin application across the project lifecycle: from ideation to theoretical deep dive to proof of concept to large-scale experiment to real-world deployment at scale. This paper is an attempt to define a digital twin open architecture framework that comprises a digital twin technology stack (D-Arc) coupled with information flow, sequence, and object diagrams. Those artifacts can be used by energy sector engineers and researchers to use any digital twin platform to drive research and engineering. This paper also provides critical details related to cybersecurity aspects, data management processes, and relevant energy sector use cases.
Digital twin (DT) is an emerging concept that is gaining attention in various industries. It refers to the ability to clone a physical object (PO) into a software counterpart. The softwarized object, ...termed logical object, reflects all the important properties and characteristics of the original object within a specific application context. To fully determine the expected properties of the DT, this article surveys the state-of-the-art starting from the original definition within the manufacturing industry. It takes into account related proposals emerging in other fields, namely augmented and virtual reality (e.g., avatars), multiagent systems, and virtualization. This survey thereby allows for the identification of an extensive set of DT features that point to the "softwarization" of POs. To properly consolidate a shared DT definition, a set of foundational properties is identified and proposed as a common ground outlining the essential characteristics (must-haves) of a DT. Once the DT definition has been consolidated, its technical and business value is discussed in terms of applicability and opportunities. Four application scenarios illustrate how the DT concept can be used and how some industries are applying it. The scenarios also lead to a generic DT architectural model. This analysis is then complemented by the identification of software architecture models and guidelines in order to present a general functional framework for the DT. This article, eventually, analyses a set of possible evolution paths for the DT considering its possible usage as a major enabler for the softwarization process.
Digital Twins are new solution elements to enable ongoing digital monitoring and active functional improvement of interconnected products, devices and machines. In addition, benefits of horizontal ...and vertical integration in manufacturing are targeted by the introduction of Digital Twins. Using the test environment of smart factory cells, this paper investigates methodological, technological, operative, and business aspects of developing and operating Digital Twins. The following Digital Twin dimensions are considered in scientific and application oriented analysis: (1) integration breadth, (2) connectivity modes, (3) update frequency, (4) CPS intelligence, (5) simulation capabilities, (6) digital model richness, (7) human interaction, and (8) product lifecycle. From this, design elements for the development of Digital Twins are derived and presented.
As a key enabling technology of Industry 4.0, Digital Twin (DT) has been widely applied to various industrial domains covering different lifecycle phases of products and systems. To fully realize the ...Industry 4.0 vision, it is necessary to integrate multiple relevant DTs of a system according to a specific mission. This requires integrating all available data, information and knowledge related to the system across its entire lifecycle. It is a challenging task due to the high complexity of modern industrial systems. Semantic technologies such as ontology and knowledge graphs provide potential solutions by empowering DTs with augmented cognitive capabilities. The Cognitive Digital Twin (CDT) concept has been recently proposed which reveals a promising evolution of the current DT concept towards a more intelligent, comprehensive, and full lifecycle representation of complex systems. This paper reviews existing studies relevant to the CDT concept, and further explores its definitions and key features. To facilitate CDT development, a reference architecture is proposed based on the RAMI4.0 and some other existing architectures. Moreover, some key enabling technologies and several application scenarios of CDT are introduced. The challenges and opportunities are discussed in the end to boost future studies.
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, ...decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This first paper presents a thorough literature review of digital twin trends across many disciplines currently pursuing this area of research. Then, digital twin modeling and twinning enabling technologies are further analyzed by classifying them into two main categories: physical-to-virtual, and virtual-to-physical, based on the direction in which data flows. Finally, this paper provides perspectives on the trajectory of digital twin technology over the next decade, and introduces a few emerging areas of research which will likely be of great use in future digital twin research. In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared. Code and preprocessed data for generating all the results and figures presented in the battery digital twin case study in part 2 of this review are available on
Github
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Industrial Internet of Things (IoT) enables distributed intelligent services varying with the dynamic and realtime industrial environment to achieve Industry 4.0 benefits. In this article, we ...consider a new architecture of digital twin (DT) empowered Industrial IoT, where DTs capture the characteristics of industrial devices to assist federated learning. Noticing that DTs may bring estimation deviations from the actual value of device state, a trusted-based aggregation is proposed in federated learning to alleviate the effects of such deviation. We adaptively adjust the aggregation frequency of federated learning based on Lyapunov dynamic deficit queue and deep reinforcement learning (DRL), to improve the learning performance under the resource constraints. To further adapt to the heterogeneity of industrial IoT, a clustering-based asynchronous federated learning framework is proposed. Numerical results show that the proposed framework is superior to the benchmark in terms of learning accuracy, convergence, and energy saving.
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