mRMR 特征筛选和随机森林的故障诊断方法研究

针对滚动轴承原始振动信号重要特征信息被较强背景噪声淹没以及提取的时域特征冗余度较高、相关性较强的缺点，提出一种基于最大相关-最小冗余(max-relevance and min-redundancy, mRMR)特征筛选和随机森林的滚动轴承故障诊断研究方法。首先将原始信号进行自适应噪声完整集成经验模态分解(CEEMDAN)得到一系列固有模态分量(IMFs),分析IMF并去掉高频噪声和一部分虚假分量，再将信号进行重构并提取其时域特征，通过mRMR去除冗余性和相关性较高的特征向量，使筛选出的特征子集与标签有最大的依赖性，最后将该特征子集输入到随机森林分类器进行分类。实验表明，mRMR具有优良的特征搜索策略，重要特征均靠前得到选取，仅需3个特征便能达到较高的分类准确率，效率高于其余特征选择算法。

Research on fault diagnosis method based on mRMR feature screening and random forest

Aiming at the shortcomings that the important feature information of the original vibration signal of the rolling bearing is submerged by strong background noise, and the extracted time domain features have high redundancy and strong relevance, this paper proposes a new rolling bearing fault diagnosis research method based on maximum relevance-minimum redundancy ( mRMR) feature selection and random forest. First, the original signal is subjected to complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to obtain a series of intrinsic modal functions ( IMFs), analyze IMF and remove high frequency noise and part of false component, then reconstruct the signal and extract its time domain characteristics, mRMR is used to remove redundant and highly correlated feature vectors, so that the selected feature subset has the greatest dependence on the label, and finally the feature subset is input to the random forest classifier for classification. Experiments show that mRMR has an excellent feature search strategy, the important features are selected first. Only three features are needed to achieve a higher classification accuracy, and the efficiency is higher than other feature selection algorithms.