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PREMIuM: Big data platform for enabling self-healing manufacturing

 
: Stojanovic, Ljiljana; Stojanovic, N.

:

Jardim-Gonçalves, Ricardo (Ed.) ; Institute of Electrical and Electronics Engineers -IEEE-:
23rd International Conference on Engineering, Technology and Innovaiton, ICE/ITMC 2017. Proceedings : 27-29 June 2017, Madeira Island, Portugal
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5386-0774-9
ISBN: 978-1-5386-0775-6
S.1501-1508
International Conference on Engineering, Technology and Innovation (ICE) <23, 2017, Madeira>
Englisch
Konferenzbeitrag
Fraunhofer IOSB ()
industry data analytic; self-healing manufacturing; big data platform

Abstract
The role of big data analytics in modern manufacturing has been proven, esp. in the area of improving process efficiency. This paper goes a step forward and introduces requirements/challenges for enabling a continuous process improvement. It means that a manufacturing system will be able not only to react on a problem at hand, but rather to sense the problem (in advance) and proactively resolve the situation. In this paper, we focus on a specific type of self-adaptive manufacturing systems, which are able to resolve the problems with the equipment in an autonomous way (the so-called self-healing). The paper describes the detailed design of the platform called PREMIuM for realizing self-healing manufacturing. The platform is based on our existing work related to the big data analytics, in particular D2Lab framework for developing industry data analytics solutions. We provide also some insights from an initial validation study.

: http://publica.fraunhofer.de/dokumente/N-487543.html