In double pulse metal inert gas (DPMIG) welding, the input broadband electrical signals are often affected by strong noise, which will decrease the quality monitoring accuracy. Therefore, a suitable method should be applied to extract features from the signals. However, due to the Gibbs phenomenon and the interpolation of extreme points, former methods such as variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD) will generate unavoidable error. Therefore, broadband mode decomposition (BMD) method is newly proposed in this paper by constructing an associative dictionary library consisting of typical broadband and narrowband signals. Therefore, the drawbacks of the former methods can be avoided by searching in the dictionary. Analysis results indicate that by combining with flexible convex hulls (MMC-FCH), BMD is more accurate in extracting broadband components. Meanwhile, the mean accuracy of quality monitoring can be increased from 92.19% (VMD) and 93.75% (EEMD) to 100% by applying BMD.

在双脉冲金属惰性气体（DPMIG）焊接中，输入的宽带电信号经常受到强噪声的影响，这会降低质量监测的准确性。因此，应采用合适的方法从信号中提取特征。但是，由于吉布斯现象和极值点的插值，诸如变分模式分解（VMD）和整体经验模式分解（EEMD）之类的先前方法将产生不可避免的误差。因此，本文通过构造一个由典型的宽带和窄带信号组成的关联字典库，提出了宽带模式分解（BMD）方法。因此，可以通过在字典中搜索来避免前一种方法的缺点。分析结果表明，结合柔性凸包（MMC-FCH），BMD在提取宽带分量方面更为准确。同时，通过应用BMD，可以将质量监控的平均准确性从92.19％（VMD）和93.75％（EEMD）提高到100％。