在提出基于灰度-梯度共生矩阵焊缝缺陷聚类分析方法的基础上，为进一步识别焊缝缺陷，以焊道上分布圆柱体缺陷、焊道上分布矩形槽缺陷、热影响区分布矩形槽缺陷漏磁图像为试验对象，将灰度-梯度共生矩阵提取上述3种缺陷的漏磁图像特征量传递给层次聚类，利用k-均值聚类方法分析层次聚类选取的特征量。结果显示，这3种焊缝缺陷的识别率在93.33%以上，试验结果验证了该方法在不同类型与不同位置焊缝缺陷识别分析的可行性，焊缝不同位置相同类型缺陷的识别较焊缝相同位置不同类型缺陷识别容易。

Based on the proposed gray-gradient co-occurrence matrix (GGCM) and the clustering analysis for the weld defect, magnetic flux leakage (MFL) images of the cylinder defects in the weld, the rectangular slot defects in the weld, and the rectangular slot defects in the heat affected zone were respectively taken as the research objects to further identify the weld defect. The features for these three state MFL images extracted by GGCM were passed to hierarchical clustering, and the characteristics selected by this clustering were analyzed by using k-means clustering method. Results show that the recognition rate of weld defects by these three kinds of methods is more than 93.33%. And test results verified the feasibility of this method in different types of weld defects and the weld defects in different locations. It also showed that identifying weld defects of the same type in different locations was much easier than that of different type defects in the same position.