A digital twin framework for predictive maintenance in industry 4.0

Mihai, Stefan, Davis, William, Hung, Dang Viet, Trestian, Ramona ORCID: https://orcid.org/0000-0003-3315-3081, Karamanoglu, Mehmet ORCID: https://orcid.org/0000-0002-5049-2993, Barn, Balbir ORCID: https://orcid.org/0000-0002-7251-5033, Prasad, Raja, Venkataraman, Hrishikesh and Nguyen, Huan X. ORCID: https://orcid.org/0000-0002-4105-2558 (2021) A digital twin framework for predictive maintenance in industry 4.0. Proceedings of the 2020 International Conference on High Performance Computing & Simulation. In: HPCS 2020: 18th Annual Meeting, 22-27 March 2021, Barcelona, Spain (Online Virtual Conference). . [Conference or Workshop Item] (Accepted/In press)

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Abstract

The rapid advancements in manufacturing technologies are transforming the current industrial landscape through Industry 4.0, which refers not only to the integration of information technology with industrial production, but also to the use of innovative technologies and novel data management approaches. The target is to enable the manufacturers and the entire supply chain to save time, boost productivity, reduce waste and costs, and respond flexibly and efficiently to consumers’ requirements.

Industry 4.0 moves the digitization of manufacturing components and processes a step further by creating smart factories. Within this context, one of the key enabling technologies for Industry 4.0 is the adoption and integration of the Digital Twin (DT). However, most of the DT solutions provided by the current leading vendors are in fact digital models or digital shadows, and not digital twins. This is due to the fact that there is no common understanding of the definition of the DT amongst the leading vendors, and its usage is slightly different but showcased under the same umbrella of DT. In this paper, a DT framework is proposed that replicates the processes of a real production line for product assembly using the Festo Cyber Physical Factory for Industry 4.0 located at Middlesex University. Moreover, the paper introduces a viable framework for interlinking the physical system with its digital instance in order to offer extended predictive maintenance services and form a fully integrated digital twin solution.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 31855
Notes on copyright: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
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Depositing User: Ramona Trestian
Date Deposited: 19 Jan 2021 17:11
Last Modified: 10 Nov 2021 16:42
URI: https://eprints.mdx.ac.uk/id/eprint/31855

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