Decision Tree with Optimal Feature Selection for Bearing Fault Detection

Ngoc-Tu Nguyen; Hong-Hee Lee

Decision Tree with Optimal Feature Selection for Bearing Fault Detection

Ngoc-Tu Nguyen

Hong-Hee Lee

Vol. 8, No. 1, pp. 101-107, Jan. 2008

10.6113/JPE.2008.8.1.101

Principal Component Analysis

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Abstract

In this paper, the features extracted from vibration time signals are used to detect the bearing fault condition. The decision tree is applied to diagnose the bearing status, which has the benefits of being an expert system that is based on knowledge history and is simple to understand. This paper also suggests a genetic algorithm (GA) as a method to reduce the number of features. In order to show the potentials of this method in both aspects of accuracy and simplicity, the reduced-feature decision tree is compared with the non reduced-feature decision tree and the PCA-based decision tree.

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Cite this article

[IEEE Style]

N. Nguyen and H. Lee, "Decision Tree with Optimal Feature Selection for Bearing Fault Detection," Journal of Power Electronics, vol. 8, no. 1, pp. 101-107, 2008. DOI: 10.6113/JPE.2008.8.1.101.

[ACM Style]

Ngoc-Tu Nguyen and Hong-Hee Lee. 2008. Decision Tree with Optimal Feature Selection for Bearing Fault Detection. Journal of Power Electronics, 8, 1, (2008), 101-107. DOI: 10.6113/JPE.2008.8.1.101.