Possibilistic Bayes Modelling for Predictive Analytics

    Forskningsoutput: Kapitel i bok/konferenshandlingKonferensbidragVetenskapligPeer review

    5 Citeringar (Scopus)

    Sammanfattning

    Studies in the process industry (and also common sense) show that the most cost effective way to keep production processes running is through predictive maintenance,i.e. to carry out optimal maintenance actions just in time before a process fails. Modern processes are highly auto mated; data is collected with sensor technology that forms a big data context and offers challenges to identify coming failures from very large sets of data. Modern analytics develops algorithms that are fast and effective enough to create possibilities for optimal JIT (Just-in Time) maintenance decisions.
    OriginalspråkOdefinierat/okänt
    Titel på gästpublikationCINTI 2014 • 15th IEEE International Symposium on Computational Intelligence and Informatics
    FörlagIEEE Computer Society Institute of Electrical and Electronic Engineers
    Sidor
    ISBN (tryckt)978-1-4799-5337-0
    DOI
    StatusPublicerad - 2014
    MoE-publikationstypA4 Artikel i en konferenspublikation
    Evenemangconference; 2014-11-19; 2014-11-21 - Obuda University, Budapest, Hungary
    Varaktighet: 19 nov 201421 nov 2014

    Konferens

    Konferensconference; 2014-11-19; 2014-11-21
    Period19/11/1421/11/14

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