为提高基于旋转电弧传感器的焊缝跟踪系统的精度,提出了主成分分析（PCA）线性降维方法与关联向量机（RVM）相结合的焊缝偏差识别方法.首先,对采集到的焊接电流信号进行小波滤波,进行周期划分和数据标准化处理.然后,对采集到的焊缝偏差数据集进行主成分分析,映射到低维的PCA空间,作为关联向量机的训练样本集;最后,利用实验数据进行测试.实验结果表明：基于PCA_RVM的焊缝偏差识别方法的最大误差为0.54mm,平均误差为0.43mm;PCA_RVM的精度与普通的关联向量机法相差不大,比区间积分法、神经网络法和支持向量机法更高,其运行速度比区间积分法慢,但比神经网络法、支持向量机法和普通的关联向量机法快,所以PCA_RVM更适用于基于旋转电弧传感器的焊缝跟踪系统.

In order to improve the precision of the seam-tracking system based on rotating arc sensor,an identification method of welding seam integrating the principal component analysis（PCA） and the relevance vector machine（RVM） is proposed,marked as PCA_RVM.In this method,first,the welding current signals are processed by using a wavelet filter,followed by the cycle partition and data normalization.Then,the data set of acquired welding seam offset is analyzed via PCA and is projected in low-dimension PCA space,and the low-dimension data set is used as the training data set of RVM.The proposed method is tested by some experiments.The results show that（1） the maximum error and mean error of PCA_RVM are respectively 0.54mm and 0.43mm;（2） the precision of PCA_RVM,which is better than those of the methods based on interval integral,neural network and support vector machine,is as high as RVM;and（3） the runtime of PCA_RVM is more than that of the method based on interval integral but is less than those of the methods based on neural network,support vector machine and RVM.It is thus concluded that PCA_RVM is more suitable for the seam-tracking system based on the rotating arc sensor.