【摘要】：在超聲缺陷識別系統(tǒng)中,端點檢測是確保缺陷準(zhǔn)確識別的重要環(huán)節(jié)。為提高在實際探傷過程中端點檢測的準(zhǔn)確率,提出一種以果蠅算法優(yōu)化支持向量機的端點檢測方法。針對超聲檢測信號的特點,采用小波包變換提取反映該信號性質(zhì)的特征向量。鑒于傳統(tǒng)方法檢出率不高及支持向量機(SVM)參數(shù)難確定的問題,利用果蠅算法(FOA)優(yōu)化SVM的懲罰子和核參數(shù),提高支持向量機建模準(zhǔn)確度。試驗結(jié)果表明:FOA-SVM模型的平均檢出率達到97.5%,端點檢測效果明顯優(yōu)于傳統(tǒng)的雙門限法、普通SVM模型和GA-SVM模型。
[Abstract]:In ultrasonic defect recognition system, endpoint detection is an important step to ensure the accurate identification of defects. In order to improve the accuracy of endpoint detection in the process of practical flaw detection, an endpoint detection method using Drosophila algorithm to optimize support vector machine is proposed. According to the characteristics of ultrasonic detection signal, wavelet packet transform is used to extract the feature vector which reflects the character of the signal. Because the detection rate of traditional methods is not high and the (SVM) parameters of SVM are difficult to determine, (FOA) algorithm is used to optimize the penalty and kernel parameters of SVM to improve the accuracy of SVM modeling. The experimental results show that the average detection rate of FOA-SVM model is 97.50.The result of endpoint detection is obviously superior to that of traditional double-threshold method, ordinary SVM model and GA-SVM model.
【作者單位】： 華北電力大學(xué)自動化系;
【分類號】：TP18;TB559
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本文編號：2299192