Feature Selection Method for Hydraulic System Faults Diagnosis Based on GA-PLS

Abstract:

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Feature selection is a key step in hydraulic system fault diagnosis. Some of the collected features are unrelated to classification model, and some are high correlated to other features. These features are harmful for establishing classification model. In order to solve this problem, genetic algorithm-partial least squares (GA-PLS) is proposed for selecting the representative and optimal features. K nearest neighbor algorithm (KNN) is used for diagnosing and classifying hydraulic system faults. For expressing better performance of GA-PLS, the original data of a model engineering hydraulic system is used, and the results of GA-PLS are compared with all feature used and GA. The experimental results show that, the proposed feature method can diagnose and classify hydraulic system faults more efficiently with using fewer features.

Info:

Periodical:

Edited by:

Ran Chen

Pages:

1130-1134

DOI:

10.4028/www.scientific.net/AMM.44-47.1130

Citation:

S. Li et al., "Feature Selection Method for Hydraulic System Faults Diagnosis Based on GA-PLS", Applied Mechanics and Materials, Vols. 44-47, pp. 1130-1134, 2011

Online since:

December 2010

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Price:

$35.00

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