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中文摘要：
利用基于小波能量系数的BP神经网络方法对管道焊缝和管道凹槽进行分类识别。建立了导波检测系统。采集了管道凹槽缺陷和焊缝的多组检测信号样本，从信号样本中提取出小波能量系数，并将小波能量系数应用于BP神经网络的训练与识别。结果表明，该方法对管道缺陷的识别准确率较高，且识别效果稳定，在随机抽取信号样本进行的5次试验中，对焊缝和凹槽的最低识别准确率分别为92％和98％，最高识别准确率均为100％。
英文摘要：
The method wavelet energy coefficients combined with BP neural network is used to distinguish pipe- line grooves from welds. A guided wave detection system was established, a set of test samples of pipeline grooves from welds were collected, and wavelet energy coefficients were extracted from test samples and applied to the train- ing and recognition of BP neural network. Results show that the identification accuracy of pipeline defects of this method is high and stable with a minimum identification accuracy of 92% and 98% for weld and groove respectively, and a highest recognition accuracy of 100% in the 5 experiments on randomly chosen samples.
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