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

Author

Listed:
  • Kunyu Wang

    (Beihang University)

  • Lin Zhang

    (Beihang University)

  • Zidi Jia

    (Beihang University)

  • Hongbo Cheng

    (Beihang University)

  • Han Lu

    (Beihang University)

  • Jin Cui

    (Beihang University)

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.

Suggested Citation

  • Kunyu Wang & Lin Zhang & Zidi Jia & Hongbo Cheng & Han Lu & Jin Cui, 2024. "A framework and method for equipment digital twin dynamic evolution based on IExATCN," Journal of Intelligent Manufacturing, Springer, vol. 35(4), pages 1571-1583, April.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:4:d:10.1007_s10845-023-02125-0
    DOI: 10.1007/s10845-023-02125-0
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    References listed on IDEAS

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    1. Konstantinos Mykoniatis & Gregory A. Harris, 2021. "A digital twin emulator of a modular production system using a data-driven hybrid modeling and simulation approach," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1899-1911, October.
    2. Elisa Negri & Vibhor Pandhare & Laura Cattaneo & Jaskaran Singh & Marco Macchi & Jay Lee, 2021. "Field-synchronized Digital Twin framework for production scheduling with uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1207-1228, April.
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