Reference : Box search for the data mining of the key parameters of an industrial process
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/199919
Box search for the data mining of the key parameters of an industrial process
English
Louveaux, Quentin mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation : Optimisation discrète >]
Mathei, Axel [> >]
Mathieu, Sébastien mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids >]
2016
Intelligent Data Analysis
IOS Press
20
6
Yes (verified by ORBi)
International
1088-467X
[en] Data mining ; Integer programming ; Quality management
[en] To increase their competitiveness, many industrial companies monitor their production process, collecting large amount of measurements. This paper describes a technique using this data to improve the performance of a monitored process. In particular we wish to find a set of rules, i.e. intervals on a reduced number of parameters, for which an output value is maximized. The model-free optimization problem to solve is to find a box, restricted on a limited amount of dimensions, with the maximum mean value of the included points. This article compares a machine learning-based heuristic to the solution computed by a mixed-integer linear program on real-life databases from steel and glass manufacturing. Computational results show that the heuristic obtains comparable solutions to the mixed integer linear approach. However, the exact approach is computationally too expensive to tackle real life databases. Results show that the restriction of five process parameters, on these databases, may improve the quality of the process by 50%.
Pôle mecatech région wallonne
Virtuoso
Researchers
http://hdl.handle.net/2268/199919
10.3233/IDA-150335

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