基于多探头源数据融合的焊缝缺陷识别

Recognition of weld defects based on multi-probe source data fusion

当今的无损检测领域中,缺陷性质的识别是检测的难点,为此研究了一种基于多探头源数据融合的焊缝缺陷识别新方法.该方法通过对多探头信息的融合,提高了检测结果的可靠性及缺陷识别的准确性.选用两个不同入射角度的斜探头对含有气孔、夹渣、裂纹、未焊透和未熔合五类典型焊接缺陷的焊件分别进行了检测,提取缺陷的超声回波信号特征,构建基于特征层和决策层两级融合的多探头源缺陷智能识别分类器,实现五类焊缝缺陷的多源数据融合识别.在特征融合层采用了BP神经网络作为特征融合器,并利用其融合输出构建每个探头源的基本概率分布函数及其对每类缺陷的基本概率赋值.在决策融合层利用D-S证据理论,合并每个探头源的基本概率分布函数,实现缺陷的融合智能识别.结果表明,该方法融合了多探头源的互补信息,有效的提高了缺陷的识别率,有助于焊缝质量的评定.

The recognition of defections is still a difficulty in non-destructive testing field.A new method for recognition of weld defects based on multi-probe source data fusion was proposed in this paper,which improved the reliability of detection and accuracy of defection recognition.Several welds,containing defects of hole,slag and crack,lack of penetration and lack of fusion were respectively inspected by two probes which possessed different angles of incidence.Then the ultrasonic signal features of defect echo were extracted.Finally,an intellectualized pattern classifier with two-level feature fusion and decision fusion was developed to realize the defect recognition with data fusion.BP neural network was selected as the classifier of feature fusion to obtain the basic probability function of each probe and probability value of each type of defect.Then D-S evidential theory was used to combine the probability function of each probe and to carry out the defect recognition.The results show that the multi-probe information could be effectively fused,and the recognition rate of weld defect was improved.