Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Decision Tree with Optimal Feature Selection for Bearing Fault Detection

  • Nguyen, Ngoc-Tu (School of Electrical Engineering, University of Ulsan) ;
  • Lee, Hong-Hee (School of Electrical Engineering, University of Ulsan)
  • Published : 2008.02.20
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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.

Keywords

References

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