Bearing Diagnosis Using Time-Domain Features and Decision Tree

Authors:
Hong-Hee Lee;Ngoc-Tu Nguyen;Jeong-Min Kwon
Affiliations:
School of Electrical Engineering, University of Ulsan, Ulsan, Korea;School of Electrical Engineering, University of Ulsan, Ulsan, Korea;School of Electrical Engineering, University of Ulsan, Ulsan, Korea
Venue:
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Year:
2009

Citing 3
Cited 1

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Abstract

Bearing fault detection with the aid of the vibration signals is presented. In this paper, time-domain features are extracted to indicate bearing fault, which collected from tri-axial vibration signal. Decision tree is chosen as an effective diagnostic tool to obtain bearing status. The paper also introduces principal component analysis (PCA) algorithm to reduce training data dimension and remove irrelevant data. Both original data and PCA-based data are used to train C4.5 decision tree models. Then, the result of PCA-based decision tree is compared with normal decision tree to get the best performance of classification process.