Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (5): 9-14.doi: 10.16180/j.cnki.issn1007-7820.2020.05.002

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A Method of Weld Defect Classification Based on PCA and SVM

WAN Dongyan,XU Zhenying,YANG Qing,WU Mengqi   

  1. School of Mechanical Engineering,Jiangsu University,Zhenjiang 212013,China
  • Received:2019-04-04 Online:2020-05-15 Published:2020-06-02
  • Supported by:
    National Natural Science Foundation of China(51679112)


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