Authors:
Timo Götzelmann
;
Knut Hartmann
;
Andreas Nürnberger
and
Thomas Strothotte
Affiliation:
University of Magdeburg, Germany
Keyword(s):
Spatial data mining, visualization, association rules, discovery of error causes, product improvement.
Related
Ontology
Subjects/Areas/Topics:
Advanced User Interfaces
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Graphical Interfaces
;
Graphics Architectures
;
Interactive Environments
Abstract:
The retrospective fault analysis of complex technical devices based on documents emerging in the advanced steps of the product life cycle can reveal error sources and problems, which have not been discovered by simulations or other test methods in the early stages of the product life cycle. This paper presents a novel approach to support the failure analysis through (i) a semi-automatic analysis of databases containing product-related documents in natural language (e. g., problem and error descriptions, repair and maintenance protocols, service bills) using information retrieval and text mining techniques and (ii) an interactive exploration of the data mining results. Our system supports visual data mining by mapping the results of analyzing failure-related documents onto corresponding 3D models. Thus, visualization of statistics about failure sources can reveal problem sources resulting from problematic spatial configurations.