Big Data Analytics for Industrial Process Control

Abdul Rauf Khan, Henrik Schioler, Murat Kulahci, Torben Steen Knudsen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.
Original languageEnglish
Title of host publicationInternational Conference on Emerging Technologies and Factory Automation (etfa)
Number of pages8
PublisherIEEE
Publication date2017
Article number17489120
ISBN (Print)978-1-5090-6505-9
DOIs
Publication statusPublished - 2017
Event22nd IEEE International Conference on
Emerging Technologies And Factory Automation
- Grand Resort Hotel, Limassol, Cyprus
Duration: 12 Sep 201715 Sep 2017

Conference

Conference22nd IEEE International Conference on
Emerging Technologies And Factory Automation
LocationGrand Resort Hotel
CountryCyprus
CityLimassol
Period12/09/201715/09/2017
SeriesEmerging Technologies and Factory Automation (etfa), International Conference on
ISSN1946-0759

Keywords

  • Big data analytics for industrial process
  • Cloud Computing
  • Data Mining
  • Genetic Algorithm

Cite this

Khan, A. R., Schioler, H., Kulahci, M., & Knudsen, T. S. (2017). Big Data Analytics for Industrial Process Control. In International Conference on Emerging Technologies and Factory Automation (etfa) [17489120] IEEE. Emerging Technologies and Factory Automation (etfa), International Conference on https://doi.org/10.1109/ETFA.2017.8247658