Due to the non-stationary and nonlinear characteristics of arc signal in gas metal arc welding (GMAW), results in the difference of frequency distribution. In this study, a method for evaluate weld quality based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and extreme learning machine (ELM) is proposed. First, the current signal is decomposed into intrinsic mode functions (IMFs) of different frequency bands by CEEMDAN, and then the energy entropy of IMFs is extracted. Because of the energy of each IMF under different weld quality is varies, the energy entropy and normalized energy of IMFs are used as a feature vector to classify the weld quality combined with extreme learning machine (ELM). The result shows that CEEMDAN and ELM can be used to identify the weld quality types of GMAW accurately.

由于气体金属电弧焊（GMAW）中电弧信号的非平稳和非线性特性，导致频率分布的差异。本文提出了一种基于自适应噪声（CEEMDAN）和极限学习机（ELM）的完全集成经验模式分解的焊接质量评估方法。首先，通过CEEMDAN将电流信号分解为不同频带的本征函数（IMF），然后提取IMF的能量熵。由于不同焊接质量下每个IMF的能量是变化的，因此结合极限学习机（ELM）将IMF的能量熵和归一化能量用作特征向量来对焊接质量进行分类。结果表明，CEEMDAN和ELM可用于准确识别GMAW的焊接质量类型。