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.

Working topics:

  • 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.

Results:

  • 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.

Recommendation

Optimal Residual Stress Control – ORSC

Situation: Trend in product development where higher quality standards with tighter material property tolerances are demanded by clients. Quality problems […]

InnoZun – Improvement of the surface quality of stainless steel strip by secondary scale conditioning and innovative descaling processes

Situation: In hot processing of steel, reactions of the metal surface with ambient air result in the formation of complex […]

PreventSecDust – Reduction of dust emissions in the furnace

Initial situation: Due to the wide range of material transports, the preparation of the fluff is one of the largest […]

IConSys – Improved quality management in flat steel production

Initial situation DSS developed during DECFLAQ project is successful running since 2007 at ThyssenKrupp Rasselstein Operators are supported to check […]