摘要
鉴于分形维数对数据样本集复杂程度的定量描述特点及支持向量机在小样本集合分类和回归方面所具有的显著优势,采用信号数据序列的分形维数作为特征值,提出一种基于信号分形维数及支持向量机的点焊信号检测方法。分别对点焊飞溅缺陷和熔核尺寸缺陷建立两类支持向量机检测模型,构成支持向量机阵列,利用该阵列模型进行点焊飞溅和小尺寸熔核两种缺陷的综合检测。结果表明,该阵列对点焊缺陷的无损检测是比较精确的,能较好地无损检测到点焊过程中飞溅及小尺寸熔核两种缺陷。为点焊的无损检测提供了一种新的方法。
Because of characteristic of fractal dimension which present quantitatively describing of complexity of a sample data series and remarkable advantage of support vector machine(SVM) in small sample classification and regression, fractal dimension of signal data series is adopted as eigenvectors,and a novel detection method based on fractal and SVM is presented.Two models based on SVM are constructed.One is about flatters of spot welding and the other is about defect of nugget size.A array of SVM is consist of these two models.The array is used to detect the two defects synchronously.It is shows that this new method fits for nondestructive detection of spot welding from analysis of experiment results.This array of SVM can detect the two defects of flatters and the little nugget size better in process of spot welding.
出处
《焊接学报》
EI
CAS
CSCD
北大核心
2007年第12期38-42,共5页
Transactions of The China Welding Institution
基金
国家自然科学基金资助项目(50575159)
天津市应用基础研究计划项目(06YFJMJC03400)
关键词
分形
支持向量机
检测
点焊
飞溅
fractal
support vectors machine
detection
spot welding
flatter
