Cause&Effect Analysis of Quality Deficiencies at Steel Production using automatic Data Mining Technologies

The application of statistical methods is state-of-the-art in all steel companies worldwide to investigate data coming from technical processes. In many cases the aim of such an investigation is to find cause&effect relationships between process / plant variables and detected quality deficiencies. For these kinds of investigations special departments are responsible. They use complex statistical tools and in-house written procedures. The main disadvantage here is that the experience of the people at the production lines can not directly be used. On the other hand the mostly used uni-variate and linear statistical techniques are in many cases not sufficient to explain the behavior of the complex chain of steel production. Out of both reasons the development of automatic data mining technologies which can be handled by plant engineers without knowledge about statistics are under development at many places worldwide. This article presents some approaches of automatic and robust Data Mining which can be used by process and plant engineers.