Ultrasonic Signal Pattern Recognition of Pipeline Corrosion Defects with PSO-SVM
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摘要: 针对金属管道腐蚀问题，提出了一种基于支持向量机（Support vector machine，SVM）与粒子群优化（Particle swarm optimization，PSO）相结合的管道腐蚀缺陷的分类方法。对预处理后的超声缺陷信号进行经验模态分解（Empirical mode decomposition，EMD），提取相应的时域无量纲参数作为特征向量；建立SVM缺陷分类模型，并采用PSO算法优化SVM参数，提高模型的缺陷分类准确率。实验证明，该方法建立的模型针对不同深度的超声缺陷信号的识别率达到87.5%，优于相同试验样本下BP神经网络和RBF神经网络的分类准确率。Abstract: In order to solve the metal pipeline corrosion, a classification method of pipeline corrosion defects based on the support vector machine (SVM) and particle swarm optimization (PSO) is proposed. The time domain dimensionless parameters are extracted by using the empirical mode decomposition (EMD) for the pre-processed ultrasonic defect signals as the eigenvectors. SVM defect classification model is established, and PSO algorithm is used to optimize the SVM parameters to improve the accuracy of defect classification. The experimental results show that the recognition rate of the present model for the ultrasonic defect signals under different depths is of 87.5%, which is better than the classification accuracy of BP neural network and RBF neural network with the same experimental samples.
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表 2 缺陷样本详情
缺陷腐蚀面积/mm 缺陷腐蚀形状 缺陷最大横截面积/mm2 无缺陷 - - 2 圆形 40 5 矩形 80 8 无规则形状1 120 无规则形状2 160 无规则形状3 无规则形状4 
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表 3 缺陷回波信号数据集分布
缺陷腐蚀面积/mm 缺陷样本数量 样本信号数据数量 样本信号数据编号 无缺陷 24 240 1~240 2 24 240 241~480 5 24 240 480~720 8 24 240 720~960
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表 4 测试集识别结果
缺陷实际情况 无缺陷 2 mm 5 mm 8 mm 识别率/% 测试集预测结果 无缺陷 60 0 0 0 87.5 2 mm 0 58 9 9 5 mm 0 1 44 3 8 mm 0 1 7 48 敏感度/% 100 96.67 73.33 80
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表 5 不同寻优算法性能对比
寻优算法 识别率/% 耗时/s GA 86.67(208/240) 84.15 GS 86.25(207/240) 73.63 PSO 87.50(210/240) 53.47
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表 6 不同模型的分类性能对比
模型 识别率/% 耗时/s BP-NN 78.33(188/240) 47.55 RBF-NN 74.17(178/240) 43.97 PSO-SVM 87.50(207/240) 53.47
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