Rolling Bearing Fault Diagnosis and Prediction Method based on Gray Support Vector Machine Model

被引：2
|
作者：
Wang, Jianhua [1 ]
Kang, Taiti [1 ]
机构：
[1] Beijing Univ Technol, BJUT, Beijing, Peoples R China
来源：
2015 International Conference on Computer Science and Mechanical Automation (CSMA) | 2015年
关键词：
rolling bearing; grey model; support vector machine; fault diagnosis;
D O I：
10.1109/CSMA.2015.69
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The article put forward a method based on GM (1,1)-SVM for rolling bearing fault prediction and diagnosis. Firstly, the method extract time and frequency domain feature values of vibration signal of rolling bearing under all kinds of fault and normal condition. Then the method select important characteristic parameters to build a grey model and carry on multi step prediction; Lastly, the method use all kinds of fault and normal condition eigenvalue to train binary tree support vector machine and construct the decision tree of rolling bearing to classify the fault type.
引用
收藏
页码：313 / 317
页数：5
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