Possibilistic Bayes Modelling for Predictive Analytics

    Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

    5 Citations (Scopus)

    Abstract

    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.
    Original languageUndefined/Unknown
    Title of host publicationCINTI 2014 • 15th IEEE International Symposium on Computational Intelligence and Informatics
    PublisherIEEE Computer Society Institute of Electrical and Electronic Engineers
    Pages
    ISBN (Print)978-1-4799-5337-0
    DOIs
    Publication statusPublished - 2014
    MoE publication typeA4 Article in a conference publication
    Eventconference; 2014-11-19; 2014-11-21 - Obuda University, Budapest, Hungary
    Duration: 19 Nov 201421 Nov 2014

    Conference

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

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