:独立分量分析是近几年发展起来的基于信号高阶统计特性的分析方法,它是指从多个源信号的线性混合信号中分离出源信号的技术,但目前的算法在处理非线性变化的信号时还有一定的局限,而基于非线性函数空间的ICA方法—KICA,即核独立成分分析,可以解决这一问题。与传统的ICA方法相比,KICA方法具有更好的灵活性和鲁棒性。文章介绍了核独立分量的基本原理,并进行了仿真说明,最后结合包络阶次方法对齿轮箱实测的瞬态声音信号进行了分析,找到了故障特征,验证了该方法的有效性。
the ICA is a kind of signal processing method based on high order statistic, it can recover the source signals from the linearly mixed signals, but there are disadvantages in processing non-linearity signals, and the KICA, which is based on nonlinear function space, can solve this problem effectively. Compared to current ICA algorithms, the KICA is notable for its flexibility and robustness. We presented the principle of KICA, and illustrated it with simulations. Finally, through the analysis of transient acoustic signal on gearbox combined with order envelope spectrum analysis, we found the fault characters, thus showed its effectiveness.