测控测试
基于EMD和GA-SVM的超声检测缺陷信号识别
2016-03-03
摘  要:为提高金属探伤时对缺陷的识别能力,提出一种遗传优化支持向量机,结合经验模态分解(EMD),对超声波缺陷信号进行自动识别。首先进行经验模态分解法分解,提取出原始信号特征,构建特征向量。鉴于常用的神经网络模型识别率不高及支持向量机参数难确定的问题,利用遗传算法优化支持向量机模型(GA-SVM)的惩罚因子和核参数,提高支持向量机建模精度。分别采用神经网络模型、SVM模型和GA-SVM模型对特征向量进行训练与测试,GA-SVM模型识别率达到98.437 5%,优于神经网络方法和未改进的交叉验证法SVM模型。试验结果表明:遗传算法能有效提高支持向量机的性能,在小样本条件下能够提高超声缺陷的识别率。
关键词:缺陷信号识别;遗传算法;支持向量机;经验模态分解
文献标志码:A       文章编号:1674-5124(2016)01-0102-05
Flaw signal identification in ultrasonic testing based on EMD and GA-SVM
LI Dazhong, ZHAO Jie
(Dept of Automation,North China Electric Power University,Baoding 071003,China)
Abstract: In order to improve the flaw-recognizing ability in crack detection, a genetic algorithm optimization support vector machine (GA-SVM) has been proposed to identify automatically the ultrasonic defect signals in combination with the empirical model decomposition (EMD). First, the EMD is applied to extract the features of original ultrasonic signals and create feature vectors. Considering that common neural network models are low in recognition rate the SVM parameters are difficult to determine, the penalty factor and kernel parameter of the GA-SVM were employed to enhance the modeling precision of the GA-SVM. The feature vectors are trained and tested with the neural network model, SVM model and GA-SVM model. The recognition rate of the GA-SVM model is up to 98.437 5%, higher than the neural network model and the unimproved cross validation SVM model. Experimental results show that genetic algorithm can improve SVM performance. This machine can increase the recognition rate of ultrasonic defects in small samples.
Keywords: flaw signal recognition; genetic algorithm; SVM; EMD