Show simple item record

dc.contributor.authorSahal, Radhya
dc.contributor.authorAlsamhi, Saeed H.
dc.contributor.authorBreslin, John G.
dc.contributor.authorBrown, Kenneth N.
dc.contributor.authorAli, Muhammad Intizar
dc.identifier.citationSahal, R.; Alsamhi, S.H.; Breslin, J.G.; Brown, K.N.; Ali, M.I. (2021) Digital twins collaboration for automatic erratic operational data detection in industry 4.0. Applied Sciences. 11, 3186.
dc.description.abstractDigital 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.en_US
dc.relation.ispartofApplied Sciencesen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectDigital twinsen_US
dc.subjectOperational dataen_US
dc.subjectIndustry 4.0en_US
dc.subjectProduction systemsen_US
dc.titleDigital twins collaboration for automatic erratic operational data detection in industry 4.0en_US
dc.contributor.affiliationAthlone Institute of Technologyen_US
dc.contributor.sponsorScience Foundation Ireland (SFI) under Grant Number SFI/16/RC/3918 (Confirm), and Marie SkłodowskaCurie grant agreement No. 847577 co-funded by the European Regional Development Fund.en_US
dc.subject.departmentSoftware Research Institute AITen_US

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International