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杜健辉, 石永华, 王国荣, 黄国兴. 基于PCA_Nu-SVR的水下焊缝偏差识别方法[J]. 焊接学报, 2011, (3): 21-24.
引用本文: 杜健辉, 石永华, 王国荣, 黄国兴. 基于PCA_Nu-SVR的水下焊缝偏差识别方法[J]. 焊接学报, 2011, (3): 21-24.
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DU Jianhui, SHI Yonghua, WANG Guorong, HUANG Guoxing. Seam offset identification of underwater arc welding using PCA_Nu-SVR[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2011, (3): 21-24.
Citation: DU Jianhui, SHI Yonghua, WANG Guorong, HUANG Guoxing. Seam offset identification of underwater arc welding using PCA_Nu-SVR[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2011, (3): 21-24.
shu

基于PCA_Nu-SVR的水下焊缝偏差识别方法

Seam offset identification of underwater arc welding using PCA_Nu-SVR

    摘要
  • 摘要: 为了实现基于旋转电弧传感器的水下自动焊接,并获得良好的跟踪精度,必须研究水下的焊缝偏差识别算法.首先对采集到的焊接电流信号进行小波滤波和中值滤波,然后进行周期化和数据归一化处理.将主成分分析(PCA)线性降维方法与支持向量回归机(Nu-SVR)回归算法相结合,利用PCA对输入的样本数据进行主成分分析,消除输入波形数据间的自相关性,并作为支持向量回归机的输入.结果表明,基于PCA_Nu-SVR的焊缝偏差识别算法比区间积分法和神经网络法具有更好的识别效果,精度与支持向量机法相差不大;在运算速度上,比区间积分法慢,但比神经网络法和支持向量机法高.

     

    Abstract: In order to realize the underwater auto-welding based on rotating arc sensor and get high accuracy tracking,it is necessary to study the seam offset identification algorithm.First,the wavelet and median filter methods were used to process welding current signals,and then the signal was divided into cycle and normalized.PCA was used to remove the self-correlation of data set and reduce the number of inputs of Nu-SVR.The result showed that the maximum error and mean error of PCA_Nu-SVR was 0.95 mm and 0.65 mm.The precision of PCA_Nu-SVR was as good as Nu-SVR,and better than interval integral method and neural network.The runtime of PCA_Nu-SVR was more than interval integral method,and less than neural network and Nu-SVR.

     

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