Aspects of knowledge discovery in technical data

Steffen Brueckner; Stephan Rudolph

doi:10.1117/12.460218

12 March 2002 Aspects of knowledge discovery in technical data

Steffen Brueckner, Stephan Rudolph

Proceedings Volume 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV; (2002) https://doi.org/10.1117/12.460218
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

In many engineering applications numerical software has reached a more or less satisfactory quality of predicting the system behavior. A major disadvantage with this kind of software is that it can only be used in a later step in the engineering design process since it requires a detailed system model, such as a finite element simulation model for structural mechanics. Finite element software can only give satisfying results when the complete geometry and all material parameters are specified. However, despite all the parameter definitions in such simulation models, still a severe validation effort with experiments is needed to investigate the model abstractions. Especially in the early conceptual design phase, a need for simplified modeling and the prediction of the system behavior using only little knowledge about a new design exists. This kind of conceptual knowledge can be given e.g. in simple algebraic equations. These equations can either be derived from first principles or from knowledge discovery in data of previous designs, the latter being the topic of this work. Huge amounts of experimental data have been recorded and stored by industry especially in the past ten years with microcomputers being available throughout the companies. Additionally the engineering domain, other than e.g. the business domain, has the advantage that at least a small number of planned experiments can be conducted to enhance the data quantity and quality and to validate the knowledge discovery results. This paper emphasizes the need for a modified knowledge discovery process for engineering (and other scientific domains) and shows the differences to the traditional knowledge discovery in data bases.

Citation Download Citation

Steffen Brueckner and Stephan Rudolph "Aspects of knowledge discovery in technical data", Proc. SPIE 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV, (12 March 2002); https://doi.org/10.1117/12.460218

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KEYWORDS

Data modeling

Knowledge discovery

Modeling

Data acquisition

Data processing

Process engineering

Databases

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