Detecting failure of a material handling system through a cognitive twin

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dc.contributor.authorD'Amico, Davide R.
dc.contributor.authorSarkar, A.
dc.contributor.authorKarray, H.
dc.contributor.authorAddepalli, Sri
dc.contributor.authorErkoyuncu, John Ahmet
dc.date.accessioned2023-01-26T16:10:27Z
dc.date.available2023-01-26T16:10:27Z
dc.date.issued2022-10-26
dc.description.abstractThis paper describes a methodology for developing a digital twin (DT) based on a rich semantic model and principles of system engineering. The aim is to provide a general model of digital twins (DT) that can improve decision making based on semantic reasoning on real-time system monitoring. The methodology has been tested on a laboratory pilot plant that acts as a material handling system. The key contribution of this research is to propose a generic information model for DT using foundational ontology and principles of systems engineering. The efficacy of the proposed methodology is demonstrated by the automatic detection of a component level failure using semantic reasoning.en_UK
dc.identifier.citationD'Amico RD, Sarkar A, Karray H, et al., (2022) Detecting failure of a material handling system through a cognitive twin. In: 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022, 22-24 June 2022, Nantes, Franceen_UK
dc.identifier.issn2405-8963
dc.identifier.urihttps://doi.org/10.1016/j.ifacol.2022.10.128
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19041
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDigital twinen_UK
dc.subjectcognitive twinen_UK
dc.subjectontologyen_UK
dc.subjectBFOen_UK
dc.subjectIOFen_UK
dc.subjectCCOen_UK
dc.subjectknowledge graphen_UK
dc.subjectSPARQLen_UK
dc.subjectmaterial handling systemsen_UK
dc.subjectFesto MPSen_UK
dc.titleDetecting failure of a material handling system through a cognitive twinen_UK
dc.typeConference paperen_UK
dcterms.dateAccepted2022-06-22

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