BFI VDEh-Betriebsforschungsinstitut GmbH

BFI VDEh-Betriebsforschungsinstitut GmbH
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PRESED - Predictive Sensor Data mining

Initial situation:

  • Some steelworks experiences problems with product deficiencies like slivers and cracks.
  • Several pre-studies indicated a relationship between the time dependence of a set of measured values and the occurrence frequency of the defect.
  • Methods for predicting, detecting and reducing the defect early in the process are desired.

Project targets:

  • A prediction system for the defect probability is developed to assist the plant personnel.
  • Novel results on defect appearance and root causes are expected.
  • Reduction of slivers and cracks by operating the processes at optimum working points.

Innovative approaches:

  • Highly resolved time-series process data is analysed with respect to the root causes for these defects.
  • Influential key factors are derived from the time-series data to quantify the probability of the defects.
  • An event-detection for time-series data will be developed.
  • The know-how about the interdependencies is conserved in an ontological knowledge base.
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partners

ArcelorMittalPredict by SnefAltair RapidminerRiva Forni Elettrici S.p.AUNIVERSITE PIERRE ET MARIE CURIE - UPMC

Funding reference

RFCS Nr. RFSR‐CT‐2014‐00031

Your contact person

	 Dipl.-Ing. Norbert Holzknecht

3 Dipl.-Ing. Norbert Holzknecht

+49 211 98492-602
norbert.holzknecht_at_bfi.de

VDEh-Betriebsforschungsinstitut GmbH
Sohnstrasse 69
40237 Düsseldorf
Germany

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