CO2焊接过程动态信号的特征分析

摘要

利用小波分析方法，对所采集的CO2焊接电流、电弧电压和电弧声波进行了信号降噪处理和奇异点分析，提取不同频率范围的声波能量作为表征焊接过程状态变化的特征向量。采用统计学方法研究了特征值集合与焊接飞溅的相关性，完成了特征集合的评价与降维，为实现焊接过程的在线诊断奠定了基础。%Wavelet analysis is utilized on eliminating noises and interpreting zero crossings of welding current,arc voltage and arc sound in CO2 arc welding process,which are collected by experiment system.Extracted energy of arc sound in different frequency ranges is as characteristic vectors that can indicate changes in the welding processing,The correlation between characteristic class and welding sprays is studied by statistic test to evaluate and reduce dimensions of characteristic class.The base to fulfill on-line diagnose of welding process has been advanced in this paper.