A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications

Barricelli, Barbara Rita; Casiraghi, Elena; Fogli, Daniela

doi:10.1109/access.2019.2953499

Cited by 820 publications

(550 citation statements)

References 90 publications

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“…Thanks to seamless connection and continuous interaction with their PT and with the external environment, DTs are able to continuously simulate the conditions of the PT. Simultaneously, they analyze the received data, which describes both the PT's condition and the external environment, in order to predict future statuses and trigger optimizing and/or preventive actions in case of predicted failures [1], [4].…”

Section: Research Background a Literature Review 1) Digital Twinmentioning

confidence: 99%

“…The success of DT technology in manufacturing, documented in the extensive survey reported in [4], has motivated several studies aimed at extending it to humans, by designing human DTs, that is computer models of humans tailored to any patient to allow researchers and clinicians to monitor the patient's health, for providing and test treatment protocols [4]. Human DTs differ from DTs developed and used in Industry 4.0 [5] because they are not continuously connected to their physical twin.…”

Section: Introductionmentioning

confidence: 99%

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Human Digital Twin for Fitness Management

et al. 2020

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Our research work describes a team of human Digital Twins (DTs), each tracking fitness-related measurements describing an athlete's behavior in consecutive days (e.g. food income, activity, sleep). After collecting enough measurements, the DT firstly predicts the physical twin performance during training and, in case of non-optimal result, it suggests modifications in the athlete's behavior. The athlete's team is integrated into SmartFit, a software framework for supporting trainers and coaches in monitoring and manage athletes' fitness activity and results. Through IoT sensors embedded in wearable devices and applications for manual logging (e.g. mood, food income), SmartFit continuously captures measurements, initially treated as the dynamic data describing the current physical twins' status. Dynamic data allows adapting each DT's status and triggering the DT's predictions and suggestions. The analyzed measurements are stored as the historical data, further processed by the DT to update (increase) its knowledge and ability to provide reliable predictions. Results show that, thanks to the team of DTs, SmartFit computes trustable predictions of the physical twins' conditions and produces understandable suggestions which can be used by trainers to trigger optimization actions in the athletes' behavior. Though applied in the sport context, SmartFit can be easily adapted to other monitoring tasks. INDEX TERMS Counterfactual explanations, digital twins, Internet of Things, machine learning, smart health, sociotechnical design, wearables.

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Section: Research Background a Literature Review 1) Digital Twinmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human Digital Twin for Fitness Management

et al. 2020

Self Cite

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“…Up to our current knowledge, the scientific literature does not provide a unique nor standardized definition of the Digital Twin (DT). As explained in a recent survey on the DT topic concept [3], there are works that define it as a virtual entity that can be substituted to the actual system in order to perform (at least some of) its tasks. In other works, a DT is described as a digital representation of a system for observation and prediction purposes mostly.…”

Section: A Multi-paradigm Modeling Approachmentioning

confidence: 99%

Towards a digital twin for cyber-physical production systems

Niati

Selma

Tamzalit

et al. 2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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This paper presents our early-stage research on a Multi-Paradigm Modeling (MPM) approach as an initial step towards the definition of a Digital Twin (DT) for Cyber-Physical Production Systems (CPPSs). This work takes place in the context of the digitalization of the mail sorting process at La Poste, the French national postal service company. Indeed, La Poste is currently investing on robotics modules for automatically loading mail containers. The main objective is to reduce the painful work for human operators while optimizing the robots usage. We already worked on targeting such a balance in a past effort that resulted in the production of different kinds of models of the La Poste CPPS. However, these models were defined separately and are not directly related to the underlying business process in particular. Thus, we propose an MPM approach starting from this business process as now modeled explicitly in a BPMN model. Then, we refine the high-level business activities into finer-grained activities represented in a UML Activity model. From these latest, we derive the specification of a Multi-Agent System (MAS) developed with the JADE framework and emulating the behavior of the La Poste CPPS. Our longer term objective is to pave the way for supporting the definition of a DT for this CPPS, and potentially for other CPPSs in different contexts in the future. CCS CONCEPTS • Software and its engineering → Design languages; Unified Modeling Language (UML); Software design engineering; • Computing methodologies → Agent / discrete models; • Computer systems organization → Robotic components.

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“…One major barrier to the employment of DTs is the process for building or implementing them is a very use‐case–specific endeavor, so exploration of the academic literature yields very few resources that provide interested stakeholders with a repeatable and generalizable process or strategy for employment. DTs in the literature often cover systems that are already in their operations and maintenance phase, so the development is entirely an after‐deployment consideration 27 . This ad hoc nature presents engineers and data scientists with challenges in the lack of a common process for defining requirements for the DT, an unclear path for development, and a steep learning curve for the early stages of implementation.…”

Section: Introductionmentioning

confidence: 99%

Operationalizing digital twins through model‐based systems engineering methods

Bickford

Bossuyt

Beery

et al. 2020

Systems Engineering

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In recent years there has been increased demand for readiness and availability metrics across many industries and especially in national defense to enable data‐driven decision making at all levels of planning, maintenance, and operations, and in leveraging integrated models that inform stakeholders of current operational system health and performance metrics. The digital twin (DT) has been identified as a promising approach for deploying these models to fielded systems although several challenges exist in wide adoption and implementation. Two challenges examined in this article are that the nature of DT development is a system‐specific endeavor, and the development is usually an additional effort that begins after initial system fielding. A fundamental challenge with DT development, which sets it apart from traditional models, is the DT itself is treated as a separate system, and therefore the physical asset/DT construct becomes a system‐of‐systems problem. This article explores how objectives in DT development align with those of model‐based systems engineering (MBSE), and how the MBSE process can answer questions necessary to define the DT. The key benefits to the approach are leveraging work already being performed during system synthesis and DT development is pushed earlier in a system's lifecycle. This article contributes to the definition and development processes for DTs by proposing a DT development model and path, a method for scoping and defining requirements for a DT, and an approach to integrate DT and system development. An example case study of a Naval unmanned system is presented to illustrate the contributions.

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A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications

Cited by 820 publications

References 90 publications

Human Digital Twin for Fitness Management

Human Digital Twin for Fitness Management

Towards a digital twin for cyber-physical production systems

Operationalizing digital twins through model‐based systems engineering methods

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