基于ICA相关系数和VPMCD的滚动轴承故障诊断
The Rolling Bearing Fault Diagnosis Method Based on Correlation Coefficient of Independent Component Analysis and VPMCD

将基于变量预测模型的模式识别（variable predictive model based class discriminate ,简称VPMCD）,独立分量分析（independent component analysis,简称ICA）和相关系数分析方法相结合,提出了基于ICA相关系数和VPMCD的滚动轴承故障诊断方法.首先,对不同工况下的滚动轴承振动信号分别进行独立分量分析,获得各工况信号的独立分量; 然后,提取样本与不同工况信号独立分量之间的相关系数,并以相关系数绝对值的和作为该样本的特征值; 最后,采用VPMCD分类器进行故障识别和分类。实验数据的分析结果表明，该方法能够有效应用于滚动轴承故障诊断。
Variable predictive model based class discrimination (VPMCD) is a pattern recognition method that utilizes the inner relations among characteristic values extracted from the original data. In this paper, VPMCD and independent component analysis (ICA) are combined with the correlation coefficient in order to diagnosis the rolling bearing fault. First, the ICA is used to analyze vibration signals with different fault categories, and the independent components are extracted from each category. Second, the correlation coefficients are extracted from the samples and independent components of each category. The sum of the absolute values of the correlation coefficients is used as a characteristic value. Finally, the VPMCD classifier is used to recognize and classify the faults. The experimental results show that this method can be effectively applied to rolling bearing fault diagnosis.