基于PSO-SVM模型的焊接转子环焊缝超声缺陷识别

Ultrasonic Defect Recognition for Circumferential Joints of Welded Rotors Based on PSO-SVM Model

提出了一种粒子群算法(PSO)优化支持向量机(SVM)的方法,对焊接转子环焊缝的超声回波信号进行缺陷识别.对消噪后的超声回波缺陷信号进行4层小波包分解及结点重构,提取结点重构信号中近似部分的波峰系数和波形系数,并与细节部分的积分超声值、有效值和绝对值方差组成样本的特征向量;采用PSO算法对SVM的惩罚因子和核函数参数进行优化选择,最后完成缺陷识别.结果表明:PSO-SVM模型对预测样本具有很好的识别效果,与其他常用的SVM模型相比,PSO-SVM模型无论是识别率还是识别时间上都具有良好的效果.

An algorithm was proposed for defect recognition in circumferential joints of welded rotors based on PSO-SVM model. First, the denoised ultrasonic defect echo signals were decomposed by four layers wavelet packet and node reconstruction. Then, the form factor and crest factor were extracted from the ap- proximate portion of node reconstructed signals, on which basis, the sample feature vector was formed in combination with the ultrasound integral value and the absolute value of variance as well as the RMS in de- tails. Finally, the particle swarm optimization （PSO） algorithm was used to optimize the penalty factor and kernel function of support vector machine （SVM）, thus completing the defect recognition. Results show that the PSO-SVM model has good recognition performance in the prediction of samples. Comparing with other commonly used SVM models, the PSO-SVM model has advantages in both recognition rate and recognition time.