Support Vector Machine and Contourlet Transform for Analog Circuit Fault Diagnosis

被引：0
|
作者：
Liu Xiaoqin [1 ]
Huang Kaoli [1 ]
Li Gang [1 ]
Lian Guangyao [1 ]
机构：
[1] Ordnance Engn Coll, Dept Missile Engn, Shijiazhuang 050003, Peoples R China
来源：
PROCEEDINGS OF THE SECOND INTERNATIONAL SYMPOSIUM ON TEST AUTOMATION & INSTRUMENTATION, VOL. 3 | 2008年
关键词：
Analog circuit fault diagnosis; contourlet decomposition; feature selection; support vector machine;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A method for analog circuit fault diagnosis with the combination of contourlet decomposition and support vector machine is proposed in the paper. This method mainly consists of four steps: (1) 3 levels contourlet transform is applied to decompose voltage signals and extract fault feature vectors. (2) In order to effectively choose points of interest from the feature vector, a feature selection procedure is applied. (3) Training and testing samples are set up. (4) A SVM faults classifier is established to realize the fault diagnosis. The experimental results showed that the presented method achieved good classification results and had a high adaptability for fault.
引用
收藏
页码：1708 / 1712
页数：5
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