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Big data analytics for industrial process control
Department of Electronic Systems, Aalborg University, Denmark.
Department of Electronic Systems, Aalborg University, Denmark.
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering. Dept Applied Mathematics and Computer Science, Technical university of Denmark .ORCID iD: 0000-0003-4222-9631
Department of Electronic Systems, Aalborg University, Denmark.
2018 (English)In: IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Piscataway, NJ: IEEE, 2018, Vol. Part F134116Conference paper, Published paper (Refereed)
Abstract [en]

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.
Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2018. Vol. Part F134116
Series
IEEE International Conference on Emerging Technologies and Factory Automation, ISSN 1946-0759
National Category
Reliability and Maintenance
Research subject
Quality Technology and Management
Identifiers
URN: urn:nbn:se:ltu:diva-67639DOI: 10.1109/ETFA.2017.8247658ISI: 000427812000093Scopus ID: 2-s2.0-85044478403ISBN: 978-1-5090-6505-9 (electronic)ISBN: 978-1-5090-6506-6 (print)OAI: oai:DiVA.org:ltu-67639DiVA, id: diva2:1182507
Conference
22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Limassol, Cyprus, September 12-15, 2017
Available from: 2018-02-13 Created: 2018-02-13 Last updated: 2018-04-06Bibliographically approved

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CiteExportLink to record
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