Share Email Print
cover

Proceedings Paper

Feature selection for partial discharge diagnosis
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

In design of partial discharge (PD) diagnostic systems, finding a set of features corresponding to an optimal classification performance (accuracy and reliability) is critical. A diagnostic system designer typically does not have much difficulty to obtain a decent number of features by applying different feature extraction methods on PD measurements. However, the designer often faces challenges in finding a set of features that give optimal classification performance for the given PD diagnosis problem. The primary reasons for that are: a) features cannot be evaluated individually since feature interaction affects classification performance more significantly than features themselves; and b) optimal features cannot be obtained by simply combining all features from different feature extraction methods since there exist redundant and irrelevant features. This paper attempts to address the challenge by introducing feature selection to PD diagnosis. Through an example this paper demonstrates that feature selection can be an effective and efficient approach for systematically finding a small set of features that correspond to an optimal classification performance of PD diagnostic systems.

Paper Details

Date Published: 9 May 2005
PDF: 10 pages
Proc. SPIE 5768, Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems IV, (9 May 2005); doi: 10.1117/12.599819
Show Author Affiliations
Weizhong Yan, GE Global Research Ctr. (United States)
Kai F. Goebel, GE Global Research Ctr. (United States)


Published in SPIE Proceedings Vol. 5768:
Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems IV
Tribikram Kundu, Editor(s)

© SPIE. Terms of Use
Back to Top