拌摩擦焊焊缝缺陷超声检测信号特征分析与神经网络模式识别  

Characteristic Extraction Based on Wavelet Packet and Pattern Recognition for Ultrasonic Inspection Signals from Defects in FSW Joints Using Artificial Neural Network

作　　者：徐蒋明[1] 柯黎明[2] Xu Jiangming;Ke Liming(Nuclear Power Institute of China,Chengdu,610213,China;School of Aeronautical Manufacturing Engineering,Nanchang Hangkong University,Nanchang,330063,China)

机构地区：[1]中国核动力研究设计院,成都610213 [2]南昌航空大学航空制造工程学院,南昌330063

出　　处：《核动力工程》2020年第1期163-166,共4页Nuclear Power Engineering

摘　　要：以搅拌摩擦焊(FSW)焊缝的包铝层伸入、未焊透、隧道孔缺陷为对象,将小波分析理论应用于缺陷超声检测信号特征提取问题的研究,使用小波包分解重构节点能量、小波包分解节点系数、缺陷信号的功率谱密度小波分解这三种方法对缺陷的超声检测信号进行特征提取。利用类别可分离性判据和BP神经网络分别对提取的特征量进行评估和识别。结果表明,缺陷信号的功率谱密度小波分解这一特征提取方式具有最好的类别可分性,并且以该特征量为网络输入的BP神经网络具有85.71%缺陷识别率。The paper regards clad defect, channel defect and lack of penetration(LOP) in the FSW joints as object, makes research on application of wavelet analysis theory in feature extraction, and uses the three feature extraction methods based on wavelet packet(WP) signal component node energy, WP node coefficients, wavelet decomposition of the power spectral density(PSD) of the defects echo signal to extract the features of the three types of defects. To assess the classification performance of the feature extraction methods above by classification criteria based on Euclidean’s distance, then the features can be loaded to the artificial neural network(ANN) that is used for recognition of the defects. The result shows that the feature extraction method based on wavelet decomposition of the PSD of the defects echo signal has the best classification performance, and the ANN that use the feature gets the rate of defects recognition 85.71%.

关 键 词：搅拌摩擦焊(FSW) 超声检测 小波分析 神经网络 

分 类 号：TB553[理学—物理] TG441.7[理学—声学]