基于混合特征和支持向量机的抽油杆缺陷识别  

Recognition Based on Composite Characteristics and SVM for Sucker Rod's Defects

作　　者：孙红春[1] 谢里阳[1] 邢海涛[2] 

机构地区：[1]东北大学机械工程与自动化学院,辽宁沈阳110004 [2]华润雪花啤酒(辽宁)有限公司,辽宁沈阳110021

出　　处：《东北大学学报（自然科学版）》2009年第2期266-269,共4页Journal of Northeastern University(Natural Science)

基　　金：教育部高等学校博士学科点专项科研基金资助项目(20050145027)

摘　　要：为了提高抽油杆的缺陷识别率,将小波包能量特征和时域峰峰值特征组成的混合特征向量和基于小样本的支持向量机法应用于抽油杆的缺陷识别中.应用基于类距离的可分离性判据,证明了混合特征比单一小波包能量特征的可分离性强,在一定程度上可提高抽油杆缺陷识别的有效性;同时应用大量的数据和一对一分类的支持向量机进行抽油杆缺陷模式识别.其识别结果表明,混合特征具有比单一小波包能量特征更好的分离性,识别缺陷的泛化误差小,提高了抽油杆的缺陷识别率.To improve the recognition rate of sucker rod＇s defects, the composite characteristics including both the characteristics of wavelet packet energy and peak-to-peak values in time domain were applied to the recognition in combination with the SVM based on small samples. The separability of the composite characteristics was proved better than that of the characteristics of single wavelet packet energy and the former can enhance the effectiveness of recognition to a certain extent by introducing the separability criterion based on the distance between classes. On the other hand, the pattern recognition of the sucker rod＇s defects was carried out with the one- to-one data classified SVM using lots of data, and the results revealed that the separability of composite characteristics is better than that of single wavelet packet energy, with smaller errors due to the generalization of defect recognition.

关 键 词：小波包能量特征 特征提取 模式识别 支持向量机 抽油杆 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TE933[自动化与计算机技术—控制科学与工程]