Authors:
Ari Isokangas
;
Mika Ruusunen
and
Kauko Leiviskä
Affiliation:
University of Oulu, Finland
Keyword(s):
Identification, Input selection, Characterisation, Model construction, Process control.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Identification
;
Time Series and System Modeling
Abstract:
A framework for surveying multivariate process data is presented. Systematic procedure utilises linear model candidates constructed in sliding data windows of varying length, to determine the usefulness of data segments for process identification. The discussed survey approach was applied to an industrial wood debarking data, enabling the study of process variables and conditions affecting the wood losses. In addition, main process interactions and delays were easily discovered from the structures of the interpretable linear model candidates. The analysis can thus provide valuable information also for process modelling and control.