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基于LBP-KPCA特征提取的焊缝超声检测缺陷分类方法

胡宏伟, 张婕, 彭刚, 易可夫, 王磊

胡宏伟, 张婕, 彭刚, 易可夫, 王磊. 基于LBP-KPCA特征提取的焊缝超声检测缺陷分类方法[J]. 焊接学报, 2019, 40(6): 34-39. DOI: 10.12073/j.hjxb.2019400151
引用本文: 胡宏伟, 张婕, 彭刚, 易可夫, 王磊. 基于LBP-KPCA特征提取的焊缝超声检测缺陷分类方法[J]. 焊接学报, 2019, 40(6): 34-39. DOI: 10.12073/j.hjxb.2019400151
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HU Hongwei, ZHANG Jie, PENG Gang, YI Kefu, WANG Lei. Defect classification for ultrasonic inspection in weld seam based on LBP-KPCA feature extraction[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(6): 34-39. DOI: 10.12073/j.hjxb.2019400151
Citation: HU Hongwei, ZHANG Jie, PENG Gang, YI Kefu, WANG Lei. Defect classification for ultrasonic inspection in weld seam based on LBP-KPCA feature extraction[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(6): 34-39. DOI: 10.12073/j.hjxb.2019400151
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基于LBP-KPCA特征提取的焊缝超声检测缺陷分类方法

  • 长沙理工大学, 长沙 410114

基金项目: 国家自然科学基金资助项目（51205031）；湖南省自然科学基金资助项目（2018JJ2430）

Defect classification for ultrasonic inspection in weld seam based on LBP-KPCA feature extraction

  • Changsha University of Science and Technology, Changsha 410114, China

摘要

    摘要
  • 摘要: 焊缝缺陷影响结构安全，缺陷定性是实现结构安全评价的重要基础.研究了一种基于一维局部二元模式（one-dimensional local binary pattern，1-D LBP）算法结合核主成分分析（kernel principal component analysis，KPCA）提取焊缝缺陷回波信号特征的方法.采用1-D LBP算法提取缺陷回波信号的LBP特征，通过KPCA对此LBP特征集进行主成分分析，选取贡献率之和超过90%的前N个主成分作为缺陷分类的特征向量，利用基于径向基核函数的支持向量机（support vector machine，SVM）实现了缺陷类型的自动分类.以夹渣、气孔和未焊透三类焊缝缺陷为对象，开展了缺陷特征提取及分类试验.结果表明，使用LBP-KPCA特征进行缺陷分类时，准确率达到96.7%，优于常规特征，为焊缝缺陷分类及无损评价提供了重要参考.
    Abstract: Weld defects affect the structural safety and the defect classification is important for structural safety evaluation. This paper proposes a method which combines one-dimensional local binary pattern (1-D LBP) algorithm and kernel principal component analysis (KPCA) to extract the characteristics of weld defect echo signal. The 1-D LBP algorithm is used to extract the LBP features of the defect echo signal, and the principal component analysis of the LBP features set is carried out by KPCA. The top N principal components with the contribution rate of more than 90% are selected as the feature vectors for defect classification. The automatic classification of defect types is realized by support vector machine (SVM) based on radial basis function. The experiments of defect features extraction and classification were carried out with the weld defects of slag, porosity and non-penetration. The results show that the accuracy of defect classification is 96.7% when the LBP-KPCA features are used, which is superior to the conventional features. The proposed method provides an important reference for defect classification and nondestructive evaluation of weld defects.
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    • 参考文献(23)
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  • 收稿日期:  2018-08-07

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