基于核Fisher判别分析的点焊质量评估  

Quality estimation of resistance spot welding based on kernel fisher discriminant analysis

作　　者：张宏杰[1] 侯妍妍[2] 

机构地区：[1]天津工业大学天津市现代机电装备技术重点实验室,天津300160 [2]河北行政学院建筑工程系,石家庄050031

出　　处：《焊接学报》2011年第6期105-108,118,共4页Transactions of The China Welding Institution

摘　　要：通过对点焊过程电极位移信号的实时采集和特征分析,利用模糊化方法将电极位移信号映射为15×25模式矩阵.依据不同焊接电流参数下位移信号的模式特征将位移信号的模式分为5类,分别对应不同的焊点质量.以不同类别焊点的模式矩阵构建输入数据空间,利用核Fisher判别技术将低维数据空间的非线性分类问题转化为高维特征空间的线性分类问题,建立快速稳定的多类核Fisher判别分类器,实现了焊点类别的模式识别,进而实现焊点质量评估.结果表明,所提出的利用点焊过程电极位移信号模式表征焊点形核过程的方法是可行的,同时在小样本情况下,引入核技术的Fisher算法,大大提高了焊点质量评估的准确性.In this research,the displacement signal of the resistance spot welding process is monitored and mapped into a 15×25 element bipolarized matrix by fuzzy theory method.Some welded spots with different welding currents,are classified into five classes according to the prototype pattern matrices.These pattern matrices,which represent different weld quality respectively,are used to construct the input data space.By using Kernel Fisher Discriminant Analysis(KFDA) technology,a nonlinear classification problem of the original data space can be converted to a linear one in feature space of high dimension.An effective pattern recognition system is developed,which realizes quality estimation of the welded spots.The results of cross-validation test show that the method utilizing pattern matrix of the electrode displacement signal to characterize formation process of nugget is effective.At the same time,the Fisher algorithm introduced kernel technology improves the recognition accuracy of the welded spot pattern under the small sample circumstance.

关 键 词：电阻点焊 核FISHER判别分析 模式识别 质量评估 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]