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

被引：0
作者：
Hu H. [1 ]
Zhang J. [1 ]
Peng G. [1 ]
Yi K. [1 ]
Wang L. [1 ]
机构：
[1] Changsha University of Science and Technology, Changsha
来源：
Hanjie Xuebao/Transactions of the China Welding Institution | 2019年 / 40卷 / 06期
关键词：
Kernel principal component analysis; One-dimensional local binary pattern; Support vector machine; Ultrasonic nondestructive testing;
D O I：
10.12073/j.hjxb.2019400151
中图分类号：
学科分类号：
摘要：
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. © 2019, Editorial Board of Transactions of the China Welding Institution, Magazine Agency Welding. All right reserved.
引用
收藏
页码：34 / 39
页数：5
相关论文
共 23 条
  • [1] Murugan R., Venugobal P.R., Thyla P.R., Et al., Studies on the effect of weld defect on the fatigue behavior of welded structures, China Welding, 27, 1, pp. 53-59, (2018)
  • [2] Mu W., Gao J., Jiang H., Et al., Automatic classification approach to weld defects based on PCA and SVM, Insight-Non-Destructive Testing and Condition Monitoring, 55, 10, pp. 535-539, (2013)
  • [3] Hu W., Gang T., Recognition of weld defects based on multi-probe source data fusion, Transactions of the China welding Institution, 34, 3, pp. 45-48, (2013)
  • [4] Chen Y., Ma H.W., Zhang G.M., A support vector machine approach for classification of welding defects from ultrasonic signals, Nondestructive Testing and Evaluation, 29, 3, pp. 243-254, (2014)
  • [5] Gang T., Takahashi Y., Wu L., Intelligent pattern recognition and diagnosis of ultrasonic inspection of welding defects based on neural network and information fusion, Science and Technology of Welding and Joining, 7, 5, pp. 314-320, (2002)
  • [6] Drai R., Khelil M., Benchaala A., Time frequency and wavelet transform applied to selected problems in ultrasonics NDE, NDT & E International, 35, 8, pp. 567-572, (2002)
  • [7] Qu Z., Chong A.Y.B., Chacon J.L.F., Et al., Study on the laser-based weld surface flaw identification system employing wavelet analysis methodology, Research in Nondestructive Evaluation, 27, 3, pp. 137-154, (2016)
  • [8] Movafeghi A., Using empirical mode decomposition and a fuzzy algorithm for the analysis of weld defect images, Insight-Non-Destructive Testing and Condition Monitoring, 57, 1, pp. 35-39, (2015)
  • [9] Cruz F.C., Simas Filho E.F., Albuquerque M.C.S., Et al., Efficient feature selection for neural network based detection of flaws in steel welded joints using ultrasound testing, Ultrasonics, 73, pp. 1-8, (2017)
  • [10] Ojala T., Pietikainen M., Maenpaa T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis & Machine Intelligence, 7, pp. 971-987, (2002)
123