Possibilistic Bayes Modelling for Predictive Analytics

    Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

    6 Sitaatiot (Scopus)


    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.
    AlkuperäiskieliEi tiedossa
    OtsikkoCINTI 2014 • 15th IEEE International Symposium on Computational Intelligence and Informatics
    KustantajaIEEE Computer Society Institute of Electrical and Electronic Engineers
    ISBN (painettu)978-1-4799-5337-0
    DOI - pysyväislinkit
    TilaJulkaistu - 2014
    OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
    Tapahtumaconference; 2014-11-19; 2014-11-21 - Obuda University, Budapest, Hungary
    Kesto: 19 marrask. 201421 marrask. 2014


    Konferenssiconference; 2014-11-19; 2014-11-21