Possibilistic Bayes Modelling for Predictive Analytics

Christer Carlsson; Markku Heikkilä; Jozsef Mezei

doi:10.1109/CINTI.2014.7028671

Possibilistic Bayes Modelling for Predictive Analytics

Christer Carlsson, Markku Heikkilä, Jozsef Mezei

Forskningsoutput: Kapitel i bok/konferenshandling › Publicerad konferensartikel › Vetenskaplig › Peer review

6 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.

Originalspråk	Odefinierat/okänt
Titel på värdpublikation	CINTI 2014 • 15th IEEE International Symposium on Computational Intelligence and Informatics
Förlag	IEEE Computer Society Institute of Electrical and Electronic Engineers
Sidor	–
ISBN (tryckt)	978-1-4799-5337-0
DOI	https://doi.org/10.1109/CINTI.2014.7028671
Status	Publicerad - 2014
MoE-publikationstyp	A4 Artikel i en konferenspublikation
Evenemang	conference; 2014-11-19; 2014-11-21 - Obuda University, Budapest, Hungary Varaktighet: 19 nov. 2014 → 21 nov. 2014

Konferens

Konferens	conference; 2014-11-19; 2014-11-21
Period	19/11/14 → 21/11/14

Åtkomst av dokument

10.1109/CINTI.2014.7028671

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7028671

Citera det här

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title = "Possibilistic Bayes Modelling for Predictive Analytics",

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author = "Christer Carlsson and Markku Heikkil{\"a} and Jozsef Mezei",

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doi = "10.1109/CINTI.2014.7028671",

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booktitle = "CINTI 2014 • 15th IEEE International Symposium on Computational Intelligence and Informatics",

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Possibilistic Bayes Modelling for Predictive Analytics. / Carlsson, Christer ; Heikkilä, Markku ; Mezei, Jozsef.
CINTI 2014 • 15th IEEE International Symposium on Computational Intelligence and Informatics. IEEE Computer Society Institute of Electrical and Electronic Engineers, 2014. s. –.

Forskningsoutput: Kapitel i bok/konferenshandling › Publicerad konferensartikel › Vetenskaplig › Peer review

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AB - 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.

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