基于EMD和GA-SVM的超聲檢測(cè)缺陷信號(hào)識(shí)別
中國(guó)測(cè)試李大中， 趙 杰
摘 要：為提高金屬探傷時(shí)對(duì)缺陷的識(shí)別能力，提出一種遺傳優(yōu)化支持向量機(jī)，結(jié)合經(jīng)驗(yàn)?zāi)B(tài)分解（EMD），對(duì)超聲波缺陷信號(hào)進(jìn)行自動(dòng)識(shí)別。首先進(jìn)行經(jīng)驗(yàn)?zāi)B(tài)分解法分解，提取出原始信號(hào)特征，構(gòu)建特征向量。鑒于常用的神經(jīng)網(wǎng)絡(luò)模型識(shí)別率不高及支持向量機(jī)參數(shù)難確定的問(wèn)題，利用遺傳算法優(yōu)化支持向量機(jī)模型（GA-SVM）的懲罰因子和核參數(shù)，提高支持向量機(jī)建模精度。分別采用神經(jīng)網(wǎng)絡(luò)模型、SVM模型和GA-SVM模型對(duì)特征向量進(jìn)行訓(xùn)練與測(cè)試，GA-SVM模型識(shí)別率達(dá)到98.437 5%，優(yōu)于神經(jīng)網(wǎng)絡(luò)方法和未改進(jìn)的交叉驗(yàn)證法SVM模型。試驗(yàn)結(jié)果表明：遺傳算法能有效提高支持向量機(jī)的性能，在小樣本條件下能夠提高超聲缺陷的識(shí)別率。
關(guān)鍵詞：缺陷信號(hào)識(shí)別；遺傳算法；支持向量機(jī)；經(jīng)驗(yàn)?zāi)B(tài)分解
文獻(xiàn)標(biāo)志碼：A 文章編號(hào)：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
