Digital Twins Collaboration for Automatic Erratic Operational Data Detection in Industry 4.0
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Confirm SFI Research Centre for Smart Manufacturing, National University of Ireland Galway, H91 TK33 Galway, Ireland
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School of Computer Science and Information Technology, University College Cork, T12 E8YV Cork, Ireland
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Software Research Institute, Athlone Institute of Technology, N37 W089 Athlone, Ireland
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Faculty of Engineering, IBB University, Ibb 70270, Yemen
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School of Electronic Engineering, Dublin City University, D09 V209 Dublin, Ireland
*
Author to whom correspondence should be addressed.
Academic Editor: Silvio Abrate
Appl. Sci. 2021, 11(7), 3186; https://doi.org/10.3390/app11073186
Received: 14 March 2021
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Revised: 27 March 2021
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Accepted: 30 March 2021
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Published: 2 April 2021
(This article belongs to the Special Issue Information and Communications Technology for Industry 4.0)
Digital twin (DT) plays a pivotal role in the vision of Industry 4.0. The idea is that the real product and its virtual counterpart are twins that travel a parallel journey from design and development to production and service life. The intelligence that comes from DTs’ operational data supports the interactions between the DTs to pave the way for the cyber-physical integration of smart manufacturing. This paper presents a conceptual framework for digital twins collaboration to provide an auto-detection of erratic operational data by utilizing operational data intelligence in the manufacturing systems. The proposed framework provide an interaction mechanism to understand the DT status, interact with other DTs, learn from each other DTs, and share common semantic knowledge. In addition, it can detect the anomalies and understand the overall picture and conditions of the operational environments. Furthermore, the proposed framework is described in the workflow model, which breaks down into four phases: information extraction, change detection, synchronization, and notification. A use case of Energy 4.0 fault diagnosis for wind turbines is described to present the use of the proposed framework and DTs collaboration to identify and diagnose the potential failure, e.g., malfunctioning nodes within the energy industry.
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Keywords:
digital twins; auto-detection; operational data; cyber-physical; Industry 4.0; production system
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MDPI and ACS Style
Sahal, R.; Alsamhi, S.H.; Breslin, J.G.; Brown, K.N.; Ali, M.I. Digital Twins Collaboration for Automatic Erratic Operational Data Detection in Industry 4.0. Appl. Sci. 2021, 11, 3186. https://doi.org/10.3390/app11073186
AMA Style
Sahal R, Alsamhi SH, Breslin JG, Brown KN, Ali MI. Digital Twins Collaboration for Automatic Erratic Operational Data Detection in Industry 4.0. Applied Sciences. 2021; 11(7):3186. https://doi.org/10.3390/app11073186
Chicago/Turabian StyleSahal, Radhya, Saeed H. Alsamhi, John G. Breslin, Kenneth N. Brown, and Muhammad Intizar Ali. 2021. "Digital Twins Collaboration for Automatic Erratic Operational Data Detection in Industry 4.0" Applied Sciences 11, no. 7: 3186. https://doi.org/10.3390/app11073186
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