关键词: 缺陷识别, 主成分分析, CURE聚类算法, 比对算法

Abstract: For solving the problem that fixed classification model, limited training set size and difficulty to reflect the differences of different defects and polymorphism of similar defects which conventional ultrasonic weld defect identification method exists, combined with the condition of data analysis strategy under the big data environment and the ideas of the match in genetic defect identification, the principal component analysis and CURE clustering algorithm convert the defect echo signal to the object of the matching, then the current detecting defect characteristics can match with the historical data, the nearest neighbor method implements the expansion of the historical defects data set. Through the application of Smith-Waterman alignment algorithm based on the basic space penalty on R, the simulation results verify that the defect identification method is feasible and effective to identify the porosity, slag inclusion, cracks, incomplete penetration and incomplete fusion five kinds of common defects, the method has good identification accuracy.

Key words: defects identification, principal component analysis, Clustering Using Representatives（CURE） clustering algorithm, alignment algorithm