基于典型相关分析的机械故障盲源分离方法研究

Blind Source Separation Method of Machine Fault Based on Canonical Correlation Analysis

提出一种基于典型相关分析的机械故障盲分离方法。与基于独立分量分析的故障盲分离方法相比较,传统的机械故障盲分离方法只考虑样本值的统计分布,不考虑源信号之间的时间和空间关系,而提出的方法克服了此不足,利用源信号的自相关关系来进行盲源分离。仿真结果表明:提出的方法取得了满意的分离效果,并且具有很高的的计算效率,分离速度要快于传统的盲源分离方法。最后,将提出的方法应用到轴承故障诊断中,实验结果进一步验证了该方法的有效性。

A blind source separation method of machine fault diagnosis based on canonical correlation analysis was presented. Compared with the blind source separation method of machine fault diagnosis based on independent component analysis （ICA）,only the statistical distribution of the sample values was considered in traditional blind source separation method,without regard to the time and spatial relationship between the source signals.However,in the proposed method,this defect was overcome,and the autocorrela-tion of source signal was used to separate the mixture signals.The simulation results show that using the proposed method,satisfactory separation effect and much more high computational efficiency were obtained than the traditional ICA method.Finally,the proposed method was applied to the fault diagnosis of rolling bearing.The experiment results validate the effectiveness of the proposed method.