Modeling as the Foundation of Digital Twins

Brylina, O. G.; Kuzmina, Nadezhda; Осинцев, К. В.

doi:10.1109/glosic50886.2020.9267812

Cited by 15 publications

(6 citation statements)

References 20 publications

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“…By using mathematical equations, it is possible to capture the underlying physical principles that govern the system’s behavior and simulate its response to different operating conditions. This approach has been widely used in the development of Digital Twins for various applications, including manufacturing, robotics, and aerospace, among others [ 33 , 34 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

An Interpretable Digital Twin for Self-Aware Industrial Machines

Vilar-Dias,

Junior,

Lima-Neto

2023

Sensors

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This paper presents a proposed three-step methodology designed to enhance the performance and efficiency of industrial systems by integrating Digital Twins with particle swarm optimization (PSO) algorithms while prioritizing interpretability. Digital Twins are becoming increasingly prevalent due to their capability to offer a comprehensive virtual representation of physical systems, thus facilitating detailed simulations and optimizations. Concurrently, PSO has demonstrated its effectiveness for real-time parameter estimation, especially in identifying both standard and unknown components that influence the dynamics of a system. Our methodology, as exemplified through DC Motor and Hydraulic Actuator simulations, underscores the potential of Digital Twins to augment the self-awareness of industrial machines. The results indicate that our approach can proficiently optimize system parameters in real-time and unveil previously unknown components, thereby enhancing the adaptive capacities of the Digital Twin. While the reliance on accurate data to develop Digital Twin models is a notable consideration, the proposed methodology serves as a promising framework for advancing the efficiency of industrial applications. It further extends its relevance to fault detection and system control. Central to our approach is the emphasis on interpretability, ensuring a more transparent understanding and effective usability of such systems.

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Section: Proposed Methodologymentioning

confidence: 99%

An Interpretable Digital Twin for Self-Aware Industrial Machines

Vilar-Dias,

Junior,

Lima-Neto

2023

Sensors

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“…Reference [12] proposes digital twin as a new potential technology has received much attention from both academia and industry increasingly. Digital twin technology [13] paves a way for achieving cost-efficient trial and performance-optimal management. The data fusion applied to the industrial digital twin is a typical multi-source heterogeneous data fusion, which emphasizes eliminating the differences between different data sources and providing an undifferentiated global unified view for multi-source heterogeneous data.…”

Section: Related Workmentioning

confidence: 99%

Industrial data fusion method based on semantic data dictionary for digital twin

Liu

Zhang²

2023

Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022)

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Industrial digital twin is the key support for the transformation of industry to intelligence. The realization of digital twin relies on the integration of whole-process data, which can provide real-time and intelligent decision-making optimization for production management. This paper proposes a digital twin data fusion method based on semantic data dictionary. Firstly, a modeling method of domain data dictionary is designed, and a semantic method is introduced to realize the construction of semantic data dictionary. Then, based on the semantic data dictionary, a semantic similarity calculation method based on multivariate distance weighting is proposed. Finally, the algorithm is tested and verified. The experimental results show that the method in this paper has a significant effect on automatic data fusion in the construction of industrial digital twins.

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“…The structure and timing diagrams can be used to describe its operation. The model may be valuable to decision makers or multiple stakeholders working in the field of industrial control systems (Brylina et al, 2020). Then, the system test data can be used to generate data-driven modeling.…”

Section: Types Of Digital Twinmentioning

confidence: 99%

Review of synergy between machine learning and first principles models for asset integrity management

Cai,

Fang,

Daida

et al. 2023

Front. Chem. Eng.

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The chemical process industry (CPI) accumulated a rich data asset through industrial 4.0. There is a strong drive to develop and utilize effective approaches for process performance prediction and improvement, process control, sensor development, asset management, etc. The synergy between machine learning and first principles models can bring new insights and add tremendous value to the CPI. This paper reviews various applications of the synergies towards asset integrity management. An overview of some related commercial software packages are also provided.

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Modeling as the Foundation of Digital Twins

Cited by 15 publications

References 20 publications

An Interpretable Digital Twin for Self-Aware Industrial Machines

An Interpretable Digital Twin for Self-Aware Industrial Machines

Industrial data fusion method based on semantic data dictionary for digital twin

Review of synergy between machine learning and first principles models for asset integrity management

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