针对故障特征属性值域之间存在着一定相关性导致准确分类困难的问题，提出一种能够考虑相关系数影响作用的转子故障数据集分类方法。该方法是将相关流形距离的边界Fisher分析（Correlation Manifold Distance Marginal Fisher Analysis, CDMFA）与相关流形距离的K-近邻（Correlation Manifold Distance K-Nearest Neighbor, CDKNN）分类器概念相结合在一起的结果。首先，将振动信号集合转换成多域、多通道高维故障特征数据集；然后，通过CDMFA将融合相关系数的相关流形距离用于度量数据样本间的近邻与权值，据此能更好地反映高维数据间的相似性关系，提取出能使类间距离趋大的低维特征子集。最后，将得到的低维特征子集输入到CDKNN分类器中进行故障模式辨识。用一个由双跨度转子系统数据集与仿真数据集对所提出的方法进行了验证。结果表明：本方法降维效果良好，可获得更高的故障分类准确率。研究发现，采用相关流形距离作为信息测度的故障数据分类方法能更真实地揭示出高维特征间的几何结构关系。本方法可为高维故障数据集的特征属性约简与分类，提供数据预处理的理论参考依据。
Abstract
Aiming at the problem of accurate classification is difficult which is due to a certain correlation between fault feature attribute domain, a kind of rotor fault data classification method of considering the influence of the correlation coefficient is proposed. This method is the result of concept combining the Correlation Manifold Distance Marginal Fisher Analysis (Correlation Manifold Distance Marginal Fisher Analysis, CDMFA) and Correlation Manifold Distance K - Nearest Neighbor (Correlation Manifold Distance K-Nearest Neighbor, CDKNN) classifier together. First of all, the vibration signal are converted into high-dimensional data-set of multi-domain and multi-channel. Then, using correlation manifold distance of the fused correlation coefficient to measure neighbors and weights of fault samples by the CDMFA, which can better reflect the similarity relation between high-dimensional data and extract low-dimensional feature subset of making the bigger distance between the class. Finally, the low-dimensional feature subset is input into CDKNN classifier for fault pattern recognition. The proposed method is verified by using a double span rotor system data-set and simulation data-set. The results show that the method has better dimension reduction effect and higher fault classification accuracy. The study finds that the manifold distance fault data classification method can reveal more realistic high-dimensional feature geometry relation. This method provides the theory reference of data preprocessing for feature attribute reduction and classification of the high dimensional fault data-set.
关键词
故障分类 /
相关流形距离 /
边界Fisher分析 /
K近邻分类器 /
转子故障数据集
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Key words
Fault Classification /
Correlation Manifold Distance /
Marginal Fisher Analysis (MFA) /
K-nearest neighbor (KNN) classifier /
Rotor Fault Dataset
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