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

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

6 Sitaatiot (Scopus)

Abstrakti

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.

Alkuperäiskieli	Ei tiedossa
Otsikko	CINTI 2014 • 15th IEEE International Symposium on Computational Intelligence and Informatics
Kustantaja	IEEE Computer Society Institute of Electrical and Electronic Engineers
Sivut	–
ISBN (painettu)	978-1-4799-5337-0
DOI - pysyväislinkit	https://doi.org/10.1109/CINTI.2014.7028671
Tila	Julkaistu - 2014
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisuussa
Tapahtuma	conference; 2014-11-19; 2014-11-21 - Obuda University, Budapest, Hungary Kesto: 19 marrask. 2014 → 21 marrask. 2014

Konferenssi

Konferenssi	conference; 2014-11-19; 2014-11-21
Ajanjakso	19/11/14 → 21/11/14

Pääsy asiakirjaan

10.1109/CINTI.2014.7028671

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

Viittausmuodot

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title = "Possibilistic Bayes Modelling for Predictive Analytics",

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.",

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

year = "2014",

doi = "10.1109/CINTI.2014.7028671",

language = "Odefinierat/ok{\"a}nt",

isbn = "978-1-4799-5337-0",

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

publisher = "IEEE Computer Society Institute of Electrical and Electronic Engineers",

note = "conference; 2014-11-19; 2014-11-21 ; Conference date: 19-11-2014 Through 21-11-2014",

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Carlsson, C , Heikkilä, M & Mezei, J 2014, Possibilistic Bayes Modelling for Predictive Analytics. julkaisussa CINTI 2014 • 15th IEEE International Symposium on Computational Intelligence and Informatics. IEEE Computer Society Institute of Electrical and Electronic Engineers, Sivut –, conference; 2014-11-19; 2014-11-21, 19/11/14. https://doi.org/10.1109/CINTI.2014.7028671

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. –.

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

TY - GEN

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AU - Carlsson, Christer

AU - Heikkilä, Markku

AU - Mezei, Jozsef

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N2 - 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.

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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DO - 10.1109/CINTI.2014.7028671

M3 - Konferensbidrag

SN - 978-1-4799-5337-0

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BT - CINTI 2014 • 15th IEEE International Symposium on Computational Intelligence and Informatics

PB - IEEE Computer Society Institute of Electrical and Electronic Engineers

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