Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61748
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dc.contributor.authorShafiq, Syed Imranen
dc.contributor.authorSanin, Cesaren
dc.contributor.authorSzczerbicki, Edwarden
dc.date.accessioned2024-07-22T10:35:22Z-
dc.date.available2024-07-22T10:35:22Z-
dc.date.issued2022-07-
dc.identifier.citationCybernetics and Systems, 53(5), p. 510-519en
dc.identifier.issn1087-6553en
dc.identifier.issn0196-9722en
dc.identifier.urihttps://hdl.handle.net/1959.11/61748-
dc.description.abstract<p>The entire manufacturing spectrum is transforming with the advent of Industry 4.0. The features of Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) were utilized for developing Virtual Engineering Objects (VEO), Virtual Engineering Process (VEP) and Virtual Engineering Factory (VEF), which in turn facilitate the creation of smart factories. In this study, DDNA based Machine Monitoring for Total Maintenance in Industry 4.0 framework is demonstrated. The concept of VEO is used for the Tool and Equipment Monitoring, while for the Plants Operations Monitoring and Quality Monitoring, VEP and VEF are employed. Query extraction feature of DDNA is exploited for Adaptive Control. This study shows that Machine Efficiency (ME) can be monitored along with analysis of machine KPI’s like breakdown time, setting time, and other losses. Moreover, reports can be generated efficiency-wise, breakdown-wise, operator-wise. The data of these reports is used to predict and make future decisions related to machine maintenance.</p>en
dc.languageenen
dc.publisherTaylor & Francis Incen
dc.relation.ispartofCybernetics and Systemsen
dc.titleDecisional DNA (DDNA) Based Machine Monitoring and Total Productive Maintenance in Industry 4.0 Frameworken
dc.typeJournal Articleen
dc.identifier.doi10.1080/01969722.2021.2018549en
local.contributor.firstnameSyed Imranen
local.contributor.firstnameCesaren
local.contributor.firstnameEdwarden
local.profile.schoolSchool of Science and Technologyen
local.profile.emailcmaldon3@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeUnited States of Americaen
local.format.startpage510en
local.format.endpage519en
local.peerreviewedYesen
local.identifier.volume53en
local.identifier.issue5en
local.contributor.lastnameShafiqen
local.contributor.lastnameSaninen
local.contributor.lastnameSzczerbickien
dc.identifier.staffune-id:cmaldon3en
local.profile.orcid0000-0001-8515-417Xen
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/61748en
local.date.onlineversion2021-12-22-
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleDecisional DNA (DDNA) Based Machine Monitoring and Total Productive Maintenance in Industry 4.0 Frameworken
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorShafiq, Syed Imranen
local.search.authorSanin, Cesaren
local.search.authorSzczerbicki, Edwarden
local.uneassociationNoen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.available2021en
local.year.published2022en
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/939af2d7-8e8c-45ef-9029-59b44e3947been
local.subject.for20204602 Artificial intelligenceen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.date.moved2024-07-26en
Appears in Collections:Journal Article
School of Science and Technology
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