摘要
提出了一种有效识别窄搭接焊缝缺陷的涡流检测方法。首先用经验模态分解提取焊缝涡流信号的特征参数;然后基于主成分分析对特征参数进行降维,去掉其中的冗余信息,得到焊缝的主元特征;最后将主元特征作为支持向量机的输入构建多分类器,对窄搭接焊缝的涡流实测信号进行缺陷识别。结果表明该方法准确度高、复杂度低,能有效识别焊缝的不同缺陷,具有良好的工程应用价值。
An effective eddy current testing method was presented to identify the defects of narrow lap joints.Firstly,the characteristic parameters of eddy current signals were extracted by EMD.Then,based on PCA,the characteristic parameters were reduced,the redundant informations were removed,and the main element characteristics of the welds were obtained.In the end,the main element features were used as the inputs of SVM to construct the multi-classifier,and the eddy current measurement signals of the narrow lap were identified.The results show that this method has high accuracy and low complexity.It may effectively identify different defects of weld seams,and has good engineering application values.
作者
葛亮
苗瑞
葛秋原
吴易洲
GE Liang;MIAO Rui;GE Qiuyuan;WU Yizhou(School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai,200240)
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2019年第2期225-229,共5页
China Mechanical Engineering
基金
国家自然科学基金资助项目(51435009)
关键词
涡流检测
焊缝缺陷
经验模态分解
主成分分析
支持向量机
eddy current testing
weld defect
empirical mode decomposition(EMD)
principal component analysis(PCA)
support vector machine(SVM)
