Feature selection for partial discharge diagnosis

Weizhong Yan; Kai F. Goebel

doi:10.1117/12.599819

9 May 2005 Feature selection for partial discharge diagnosis

Weizhong Yan, Kai F. Goebel

Proceedings Volume 5768, Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems IV; (2005) https://doi.org/10.1117/12.599819
Event: Nondestructive Evaluation for Health Monitoring and Diagnostics, 2005, San Diego, CA, United States

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.

Citation Download Citation

Weizhong Yan and Kai F. Goebel "Feature selection for partial discharge diagnosis", Proc. SPIE 5768, Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems IV, (9 May 2005); https://doi.org/10.1117/12.599819

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