Multiscale feature extraction and its application in the weld seam quality prediction for plasma arc welding.

As a complex thermo-physical process, the plasma arc welding (PAW) is easy to be unstable due to external interferences. Weld quality monitoring is important for intelligent robot PAW welding. Due to the different instability mechanisms, it is difficult to obtain high adaptivity and accuracy with features extracted in a single time window. In this paper, a novel feature extraction method based on sliding multiscale windows is proposed to improve model accuracy and calculation speed. A group of windows with different time widths are established to extract multiscale information. Windows slide throughout welding process and are synchronized on the timeline for feature correlation. The welding current and arc voltage are processed to extract features inside windows, including signal denoising by discrete wavelet transform (DWT) and dimension reduction by primary components analysis (PCA). Based on the feature vectors extracted from multiscale-windows, support vector machine (SVM) with radial basis function (RBF) kernel is used. The best window width is determined automatically by model training. The proposed method is used to predict weld quality for PAW in the field of shipbuilding. The results show that the model with multiscale feature extraction is helpful to improve prediction precision and recall ratio. [ABSTRACT FROM AUTHOR]