Synchronization of Industrial Plant and Digital Twin

Zipper, Holger; Diedrich, Christian

doi:10.1109/etfa.2019.8868994

Cited by 24 publications

(6 citation statements)

References 9 publications

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“…Another important characteristic of the digital twin is its continuous synchronization with the production system and its evolution, e.g., changes in wiring, physical fixation position, etc. During its life cycle, a DT must be able to synchronize with new or update engineering models and processes during Design, Production, and Operations phases [9] [10].…”

Section: Figure 2 -The Lifecycle Of a Dt And Some Needed Functions/toolsmentioning

confidence: 99%

Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models

2020

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Section: Figure 2 -The Lifecycle Of a Dt And Some Needed Functions/toolsmentioning

confidence: 99%

Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models

2020

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“…It is argued that the data from different components in different domains must be identifiable in every data model to gain a seamless transfer of data [22]. One significant problem when developing a DT is to maintain it synchronized with the real system after commissioning [23,24]. The critical requirement for having a DT of an automation system is to have a cross-domain synchronized system model of the physical workshop floor [23].…”

Section: Aspects Of Data Fusion and Integration Of Digital Twinsmentioning

confidence: 99%

“…First, any changes in the system must be detected by constant comparing of the DT and physical system's behavior. Thus, the simulation must be constantly updated according to the changes in the physical system [24]. To make a real time simulation practical, computational time needs to be low [19].…”

Section: Aspects Of Data Fusion and Integration Of Digital Twinsmentioning

confidence: 99%

Applying Digital Twin Technology in Higher Education: An Automation Line Case Study

Eriksson

Alsaleh

Far

et al. 2022

Advances in Transdisciplinary Engineering

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Production systems are being expanded to include Digital Twins (DTs) as part of increased industrial digitalization. DTs can bring benefits e.g., increase visibility, safety, and accessibility of the system. Further, digital experimentation can reduce time and cost. Though, application of DT technologies involves challenges i.e., model accuracy or errors in transferring data or codes between the DT and the physical twin. Many studies on DTs focus on industrial applications. However, DT technology has potential for implementation of digital labs in education. This aspect of DTs is of rising importance as distance education has increased over the last decade and access to physical laboratories can be restricted due to factors such as the Covid-19 pandemic. Thus, there is a need to study the use of DT technology in higher education. To address this, we investigate possibilities and challenges of applying DT technology in education to conduct industrial-like labs virtually. A case of an automation line, with full scale industrial equipment, based at a research center, is focused. Results emphasize that the application of DT technologies require multi-domain expertise to understand the consequences of every single decision in the design process on every piece of equipment involved, making the modelling process complex and time consuming. Thus, when applied in education, test procedures need to be designed to focus on students’ motivation, improved learning and understanding of production systems. DTs are considered enabling technologies supporting the concept of Industry 5.0, thus stressing the human-centric aspects of advancing Industry 4.0. The predicted application of DTs emphasizes the need for educational curricula that include laboratory applications and theoretic understanding of DT technologies. This study focusses the application of DT technologies in higher education curricula, but the result of the study can contribute to other areas such as automation and virtual commissioning towards smarter manufacturing.

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“…Calibration focuses on continuously tuning the model parameters, typically at a fixed frequency. Approaches in this category include the use of: difference/error between observation and predicted values of the state variables as objective function to optimise the selection of parameters or inputs [28,44,59]; statistical tests on simulated and real traffic flows as calibration criteria [30]; Bayesian inference in the context of unmanned aerial vehicle applications [33]; Kalman filter [1]; the error between observed output and predicted output as feedback for the calibration of clock models [31]; and online calibration by training deep learning models with streaming data (state variables) [42].…”

Section: Continuous Online Calibrationmentioning

confidence: 99%

Knowledge Equivalence in Digital Twins of Intelligent Systems

Zhang¹,

Bahsoon²,

Tziritas³

et al. 2022

Preprint

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A digital twin contains up-to-date data-driven models of the physical world being studied and can use simulation to optimise the physical world. However, the analysis made by the digital twin is valid and reliable only when the model is equivalent to the physical world. Maintaining such an equivalent model is challenging, especially when the physical systems being modelled are intelligent and autonomous. The paper focuses in particular on digital twin models of intelligent systems where the systems are knowledge-aware but with limited capability. The digital twin improves the acting of the physical system at a meta-level by accumulating more knowledge in the simulated environment. The modelling of such an intelligent physical system requires replicating the knowledge-awareness capability in the virtual space. Novel equivalence maintaining techniques are needed, especially in synchronising the knowledge between the model and the physical system. This paper proposes the notion of knowledge equivalence and an equivalence maintaining approach by knowledge comparison and updates. A quantitative analysis of the proposed approach confirms that compared to state equivalence, knowledge equivalence maintenance can tolerate deviation thus reducing unnecessary updates and achieve more Pareto efficient solutions for the trade-off between update overhead and simulation reliability. CCS Concepts: • Computer systems organization → Embedded and cyber-physical systems; Architectures; • Computing methodologies → Modeling and simulation; Knowledge representation and reasoning.

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Synchronization of Industrial Plant and Digital Twin

Cited by 24 publications

References 9 publications

Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models

Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models

Applying Digital Twin Technology in Higher Education: An Automation Line Case Study

Knowledge Equivalence in Digital Twins of Intelligent Systems

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