This PhD work proposes developing an Open and Distributed Digital Twin (DT) Model suitable for Industrial Internet of Things (IIoT) enabled smart manufacturing with emphasisondecentralized intelligence using distributedfog/edge computing and the application of intelligent asset management and predictive maintenance. The proposed Open DT frameworkwill be designed to make a seamless integration of DT Modules from potentially different Original Equipment Manufacturers (OEMs), in form of a pluggable DT. The Architecture of the Proposed DT model is aimed to factor in the application of machine learning for effective asset management, predictive maintenance and health prognostics of the Unit, System and System of System Digital Twins while adopting fog/edge architecture for cost efficiency (of computing resources).
How to Cite
Digital Twins, Fog Computing, Predictive maintenance, Machine Learning
Kazała, R., Lu ́sci ́nski, S., Str ̨aczy ́nskia, P. & Taneva, A., 2021. An Enabling Open-Source Technology for Development and Prototyping of Production Systems by Applying Digital Twinning. MDPI -Processes Journal.Knebel, F. P., Wickboldt, J. A. & Freitas, E. P. d., 2021. A Cloud-Fog Computing Architecture for Real-Time Digital Twins. Journal of Internet Services and Applications, pp. 1-9.MOYNE, A. et al., 2020. A Requirements Driven Digital Twin Framework:Specification and Opportunities. IEEE Access.QAMSANE, A. et al., 2021. A Methodology to Develop and Implement Digital Twin Solutions for Manufacturing Systems. IEEE Access.Tao, F., Zhang, M. & Nee, A., 2019. Digital Twin Driven Smart Manufacturing. s.l.:Elsevier.Teoh, Y. K., Gill, S. S. & Parlikad, A. K., 2021. IoT and Fog Computing based Predictive. IEEE Internet of Thiings Journal, pp. 1-1.Wu, Y., Zhang, K. & Zhang, Y., 2021. DigitalTwin Networks: A Survey. IEEE INTERNET OF THINGS JOURNAL, Volume 8.
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