Skoči na glavni sadržaj

Izvorni znanstveni članak

https://doi.org/10.2498/cit.1001113

Semi-automatic Maintenance of Regression Models: an Application in the Steel Industry

Ilmari Juutilainen ; Data Mining Group, Department of Electrical and Information Engineering, University of Oulu, Finland
Lauri Tuovinen ; Data Mining Group, Department of Electrical and Information Engineering, University of Oulu, Finland
Perttu Laurinen ; Data Mining Group, Department of Electrical and Information Engineering, University of Oulu, Finland
Heli Koskimäki ; Data Mining Group, Department of Electrical and Information Engineering, University of Oulu, Finland
Juha Röning ; Data Mining Group, Department of Electrical and Information Engineering, University of Oulu, Finland


Puni tekst: engleski pdf 1.042 Kb

str. 193-206

preuzimanja: 692

citiraj


Sažetak

Software applications used in the controlling and planning of production processes commonly make use of predictive statistical models. Changes in the process involve a more or less regular need for updating the prediction models on which the operational software applications are based. The objective of this article is
• to provide information which helps to design semiautomatic systems for the maintenance of statistical prediction models and
• to describe a proof-of-concept implementation in an industrial application.
The system developed processes the production data and provides an easy-to-use interface to construct updated models and introduce them into a software application. The article presents the architecture of the maintenance system, with a description of the algorithms that cause the system’s functionality. The system developed was implemented for keeping up-to-date prediction models which are in everyday use in a steel plate mill in the planning of the mechanical properties of steel products. The conclusion of the results is that the semi-automatic approach proposed is competitive with fully automatic and manual approaches. The benefits include good prediction accuracy and decreased workload of the deployment of updated model versions.

Ključne riječi

data analysis; software architecture; maintenance system; predictive modelling; model updating

Hrčak ID:

74851

URI

https://hrcak.srce.hr/74851

Datum izdavanja:

30.9.2011.

Posjeta: 1.244 *