Bearing diagnosis using time-domain features and decision tree

被引：0

作者：

Lee, Hong-Hee ^{[1
]}

Nguyen, Ngoc-Tu ^{[1
]}

Kwon, Jeong-Min ^{[1
]}

机构：

[1] Univ Ulsan, Sch Elect Engn, Ulsan 680749, South Korea

来源：

ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, PROCEEDINGS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE | 2007年 / 4682卷

关键词：

bearing diagnosis; decision tree; vibration; principal component analysis;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：952 / 960

页数：9

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