基于PCA与SVM的焊缝缺陷信号分类方法

doi:10.16180/j.cnki.issn1007-7820.2020.05.002

Abstract

Abstract:

In order to ensure that the welding structure is in safe working state, a classification method combining PCA and SVM was proposed to classify the weld feature guided wave defect signals. Firstly, based on the sparse reconstructed signal of defect echo, the feature parameter matrix of defect signal was extracted, and the dimensionality of parameter matrix was optimized by PCA to eliminate redundant information. Then, the optimized low-dimensional feature matrix was applied to SVM for classification training, and the classification effect of different principal components was compared. Kernel function and related parameters were selected to improve the accuracy of the classifier. The experimental results showed that the method could effectively classify weld defects.

Key words: weld, echo signals, defect classification, feature guided wave, PCA, SVM

CLC Number:

TN911

WAN Dongyan,XU Zhenying,YANG Qing,WU Mengqi. A Method of Weld Defect Classification Based on PCA and SVM[J].Electronic Science and Technology, 2020, 33(5): 9-14.

Figures/Tables 10

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References 16

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PCA	PC1	PC2	PC3	PC4	PC5	PC6	PC7
特征值	16.06	10.39	8.41	5.73	0.84	0.08	0.02
贡献率	0.585 5	0.195 7	0.132 5	0.072 8	0.010 8	0.001 1	0.000 4

正确率	原始参数矩阵	1主成分	3主成分	5主成分
线性核	90%	76%	78%	83%
多项式核	96%	80%	87%	92%
高斯核	94%	88%	93%	98%
Sigmoid核	60%	33%	64%	60%

缺陷类型	识别个数	识别率	总识别率
孔洞	49/50	98%	98%
裂纹	48/50	96%
无缺陷	50/50	100%

A Method of Weld Defect Classification Based on PCA and SVM

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