Data management for developing digital twin ontology model

Simple item page
dc.contributor.authorSingh, Sumit
dc.contributor.authorShehab, Essam
dc.contributor.authorHiggins, Nigel
dc.contributor.authorFowler, Kevin
dc.contributor.authorReynolds, Dylan
dc.contributor.authorErkoyuncu, John Ahmet
dc.contributor.authorGadd, Peter
dc.date.accessioned2021-01-05T16:23:07Z
dc.date.available2021-01-05T16:23:07Z
dc.date.issued2020-12-09
dc.description.abstractDigital 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 complexitiesen_UK
dc.identifier.citationSingh S, Shehab E, Higgins N, et al., (2020) Data management for developing digital twin ontology model. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Volume 235, Issue 14, December 2021, pp. 2323-2337en_UK
dc.identifier.issn0954-4054
dc.identifier.urihttps://doi.org/10.1177/0954405420978117
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16122
dc.language.isoenen_UK
dc.publisherSageen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectdata managementen_UK
dc.subjectontologiesen_UK
dc.subjectdata modellingen_UK
dc.subjectDigital twinen_UK
dc.titleData management for developing digital twin ontology modelen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Data_management_for_developing_digital_twin_ontology_mode-2020.pdf
Size:
2.56 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)