摘要
为了有效地对不同深度的局部腐蚀缺陷超声波信号进行分类识别,根据腐蚀缺陷信号样本数量较少的特点,提出了一种基于主成分分析(PCA)和支持向量机(SVM)的超声波腐蚀缺陷信号识别方法。该方法采用经验模态分解法对腐蚀缺陷信号进行分解,提取各本征模式分量的时域无量纲参数,利用主成分分析消除原始特征集中的冗余信息,降低每一个特征之间的相关性,实现腐蚀缺陷信号特征参数的降维。在PCA进行特征优化后,将支持向量机的多类分类应用于缺陷分类过程中。将腐蚀缺陷原始特征集和经主成分分析优化后的特征集,分别用于支持向量机的训练和测试,且选择不同的核函数构造支持向量机分类器。实验结果表明,基于主成分分析和支持向量机的方法可以有效地对超声波腐蚀缺陷深度信号分类。
In order to effectively identify and classify in different depth of local corrosion defect by ultrasonic signal classification,according to the characteristics of corrosion defect signals of the small number of samples,propose a method based on principal component analysis（PCA）and support vector machine（SVM）ultrasonic corrosion defect signal recognition method. The method uses empirical mode decomposition to decompose the signal of corrosion defect,extracts the non dimensional parameters of each eigenmode component in time domain,and uses Principal component analysis to eliminate redundant information in original feature set,reduce correlation between each feature,and achieve dimension reduction of corrosion defect signal feature parameters.After optimizing the features of PCA,the multi-class classification of support vector machines is applied to the process of defect classification. The original feature set of corrosion defects and the optimized feature set after principal component analysis are used for training and testing of support vector machines,respectively,and support vector machine classifier is constructed by selecting different kernel functions. The experimental results show that the method based on principal component analysis and support vector machine can effectively classify the ultrasonic corrosion defect signals.
作者
唐东林
魏子兵
潘峰
唐圳雄
李茂扬
胡琳
TANG Donglin;WEI Zibing;PAN Feng;TANG Zhenxiong;LI Maoyang;HU Lin(Key Laboratory of Ministry of Education for Oil and Gas Equipment,School of Mechatronics Engineering,Southwest Petroleum University,Chengdu 610500,China)
出处
《传感技术学报》
CAS
CSCD
北大核心
2018年第7期1040-1045,共6页
Chinese Journal of Sensors and Actuators
基金
四川省科技支撑计划项目(2017FZ0033)
关键词
主成分分析
支持向量机
经验模态分解
超声波
principal component analysis
support vector machine
empirical mode decomposition
ultrasonic wave
