A framework and method for equipment digital twin dynamic evolution based on IExATCN

Kunyu Wang, Lin Zhang (), Zidi Jia, Hongbo Cheng, Han Lu and Jin Cui
Additional contact information
Kunyu Wang: Beihang University
Lin Zhang: Beihang University
Zidi Jia: Beihang University
Hongbo Cheng: Beihang University
Han Lu: Beihang University
Jin Cui: Beihang University

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 4, No 8, 1583 pages

Abstract: Abstract Dynamic evolution is the most typical feature of a digital twin, making it different from a traditional digital model. Dynamic evolution is also the core technology for building equipment digital twins because it ensures consistency between physical space and virtual space. This paper proposes a dynamic evolution framework for black box equipment digital twins. The framework consists of three main parts: data acquisition and processing, an evolution triggering mechanism and an evolution algorithm. A formal description of the dynamic evolution of a black box digital twin is also given. Furthermore, by synthetically considering the computational accuracy and efficiency, we design an incremental external attention temporal convolution network (IExATCN) model to instantiate the proposed framework. Finally, the significance of digital twin dynamic evolution and the effectiveness of the IExATCN is verified by 3D equipment attitude estimation datasets.

Keywords: Equipment digital twin; Dynamic evolution; Modeling and simulation; Temporal convolution network (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02125-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:35:y:2024:i:4:d:10.1007_s10845-023-02125-0

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-023-02125-0

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().