SCI4climate.NRW is a research project supported by the state of North Rhine-Westphalia in connection with the IN4climate initiative to develop […]
Presed – Predictive Sensor Data mining
- 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.
- 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.
- 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.