1. University of Kragujevac Digital Archive
  2. University of Kragujevac
  3. Faculty of Engineering, Kragujevac
Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/19768
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dc.contributor.authorManojlović, Goran-
dc.contributor.authorErić, Milan-
dc.contributor.authorStefanovic, Miladin-
dc.contributor.authorNikolić, Ivica-
dc.contributor.authorMiljanić, Dragomir-
dc.date.accessioned2024-01-10T07:19:39Z-
dc.date.available2024-01-10T07:19:39Z-
dc.date.issued2012-
dc.identifier.isbn978-86-86663-82-5en_US
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/19768-
dc.description.abstractIn manufacturing, the health degradation of equipment is crucial and diagnostics should be carried out frequently. It is done with the help of statistics methodology or machine learning models. Obtained data is converted into prognostics information, so as to reduce ''down time'' to zero. ''5S'' is a step-by-step methodology used for prognostics with the aid of various computing tools for different applications in an e-manufacturing environment. It sorts out useful data from raw datasets, and converst data into information vital to equipment performance.en_US
dc.language.isoenen_US
dc.publisherFaculty of Engineering, University of Kragujevacen_US
dc.rightsinfo:eu-repo/semantics/openAccess-
dc.source6th International Quality Conference, June 2012en_US
dc.subjecthealth degradationen_US
dc.subjectprognosticsen_US
dc.subject5S methodologyen_US
dc.title''5S'' METODOLOGY IN E-MANUFACTURINGen_US
dc.typeconferenceObjecten_US
dc.description.versionPublisheden_US
dc.type.versionPublishedVersionen_US
Appears in Collections:Faculty of Engineering, Kragujevac

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