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Data management for developing digital twin ontology model

Sumit Singh,E. Shehab,E. Shehab,Nigel Higgins,Kevin Fowler,Dylan Reynolds,J. Erkoyuncu,P. Gadd

Published 2020 in Proceedings of the Institution of mechanical engineers. Part B, journal of engineering manufacture

ABSTRACT

Digital Twin (DT) is the imitation of the real world product, process or system. Digital Twin is the ideal solution for data-driven optimisations in different phases of the product lifecycle. With the rapid growth in DT research, data management for digital twin is a challenging field for both industries and academia. The challenges for DT data management are analysed in this article are data variety, big data & data mining and DT dynamics. The current research proposes a novel concept of DT ontology model and methodology to address these data management challenges. The DT ontology model captures and models the conceptual knowledge of the DT domain. Using the proposed methodology, such domain knowledge is transformed into a minimum data model structure to map, query and manage databases for DT applications. The proposed research is further validated using a case study based on Condition-Based Monitoring (CBM) DT application. The query formulation around minimum data model structure further shows the effectiveness of the current approach by returning accurate results, along with maintaining semantics and conceptual relationships along DT lifecycle. The method not only provides flexibility to retain knowledge along DT lifecycle but also helps users and developers to design, maintain and query databases effectively for DT applications and systems of different scale and complexities.

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    Proceedings of the Institution of mechanical engineers. Part B, journal of engineering manufacture

  • Publication date

    2020-12-09

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.1177/0954405420978117
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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