Big Data Analytics for Industrial Process Control

Abdul Rauf Khan; Henrik Schiøler; Murat Kulahci; Torben Knudsen

doi:10.1109/ETFA.2017.8247658

Big Data Analytics for Industrial Process Control

Abdul Rauf Khan, Henrik Schiøler, Murat Kulahci, Torben Knudsen

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

6 Citationer (Scopus)

Abstract

Today, in modern factories, each step in manufacturing produces a bulk of valuable as well as highly precise information. This provides a great opportunity for understanding the hidden statistical dependencies in the process. Systematic analysis and utilization of advanced analytical methods can lead towards more informed decisions. In this article we discuss some of the challenges related to big data analysis in manufacturing and relevant solutions to some of these challenges.

Originalsprog	Engelsk
Titel	IEEE 22nd Conference on Emerging Technologies & Factory Automation
Forlag	IEEE
Publikationsdato	2017
ISBN (Elektronisk)	978-1-5090-6505-9
DOI	https://doi.org/10.1109/ETFA.2017.8247658
Status	Udgivet - 2017
Begivenhed	IEEE Conference on Emerging Technologies & Factory Automation - Limassol, Cypern Varighed: 12 sep. 2017 → 15 sep. 2017 https://www.etfa2017.org/

Konference

Konference	IEEE Conference on Emerging Technologies & Factory Automation
Land/Område	Cypern
By	Limassol
Periode	12/09/2017 → 15/09/2017
Internetadresse	https://www.etfa2017.org/

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10.1109/ETFA.2017.8247658

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Citationsformater

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title = "Big Data Analytics for Industrial Process Control",

abstract = "Today, in modern factories, each step in manufacturing produces a bulk of valuable as well as highly precise information. This provides a great opportunity for understanding the hidden statistical dependencies in the process. Systematic analysis and utilization of advanced analytical methods can lead towards more informed decisions. In this article we discuss some of the challenges related to big data analysis in manufacturing and relevant solutions to some of these challenges.",

keywords = "Big data analytics for industrial process , Cloud Computing, Data Mining, Genetic Algorithm",

author = "Khan, {Abdul Rauf} and Henrik Schi{\o}ler and Murat Kulahci and Torben Knudsen",

year = "2017",

doi = "10.1109/ETFA.2017.8247658",

language = "English",

booktitle = "IEEE 22nd Conference on Emerging Technologies & Factory Automation",

publisher = "IEEE",

address = "United States",

note = "IEEE Conference on Emerging Technologies & Factory Automation, ETFA 2017 ; Conference date: 12-09-2017 Through 15-09-2017",

url = "https://www.etfa2017.org/",

}

TY - GEN

T1 - Big Data Analytics for Industrial Process Control

AU - Khan, Abdul Rauf

AU - Schiøler, Henrik

AU - Kulahci, Murat

AU - Knudsen, Torben

PY - 2017

Y1 - 2017

N2 - Today, in modern factories, each step in manufacturing produces a bulk of valuable as well as highly precise information. This provides a great opportunity for understanding the hidden statistical dependencies in the process. Systematic analysis and utilization of advanced analytical methods can lead towards more informed decisions. In this article we discuss some of the challenges related to big data analysis in manufacturing and relevant solutions to some of these challenges.

AB - Today, in modern factories, each step in manufacturing produces a bulk of valuable as well as highly precise information. This provides a great opportunity for understanding the hidden statistical dependencies in the process. Systematic analysis and utilization of advanced analytical methods can lead towards more informed decisions. In this article we discuss some of the challenges related to big data analysis in manufacturing and relevant solutions to some of these challenges.

KW - Big data analytics for industrial process , Cloud Computing, Data Mining, Genetic Algorithm

UR - https://etfa2017.org/technical_program

U2 - 10.1109/ETFA.2017.8247658

DO - 10.1109/ETFA.2017.8247658

M3 - Article in proceeding

BT - IEEE 22nd Conference on Emerging Technologies & Factory Automation

PB - IEEE

T2 - IEEE Conference on Emerging Technologies & Factory Automation

Y2 - 12 September 2017 through 15 September 2017

ER -

Big Data Analytics for Industrial Process Control

Abstract

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