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Proceedings Paper

Discovery of diagnostic knowledge from multisensor data
Author(s): Wojciech A. Moczulski; Jan M. Zytkow
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Paper Abstract

The paper deals with discovering qualitative and functional dependencies among attributes that describe a complex technical object. The database contains data which are values of control parameters applied in the experiment and multiple features of vibration signals. These signals can be acquired by a multi-sensor measuring system. Information carried by signals acquired from different sensors is in some sense complementary. However, since correlation between signals observed by some sensors is likely, some redundancy in the data may be achieved. Since redundancy may yield reliability and better quality of predictions, it is reasonable to take it into consideration in the model. The attempt depends on selection of the right combination of attributes and then on recursive application of the Equation Finder in order to find functional equations containing control and dependent attributes. Further on, the equations may be inverted providing the opportunity to obtain predictions of values of control attributes, that is the task of the diagnostics of the object. Such knowledge may then be applied in a diagnostic expert system.

Paper Details

Date Published: 27 March 2001
PDF: 12 pages
Proc. SPIE 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, (27 March 2001); doi: 10.1117/12.421064
Show Author Affiliations
Wojciech A. Moczulski, Silesian Univ. of Technology (Poland)
Jan M. Zytkow, Univ. of North Carolina/Charlotte (United States)


Published in SPIE Proceedings Vol. 4384:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology III
Belur V. Dasarathy, Editor(s)

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