基于全局-局部图嵌入的轴承故障诊断

Bearing Fault Diagnosis Based on Global-local Graph Embedding

传统基于图的故障诊断框架通常是利用高维数据某种结构关系构造相似图以揭示样本间的几何结构,造成数据其他结构信息丢失,无法准确提取出表征轴承运行状态的低维特征。提出了一种新的基于图的无监督特征提取方法,该方法在构造图的过程中同时考虑了高维数据的全局和局部结构,称为全局–局部图嵌入,该方法首先利用数据的全局结构信息构造一个无向图。然后,通过构造局部结构信息赋予无向图中边相应的权重,得到一个全局–局部图联合表示凸优化问题,并根据得到权重评估样本间的相似性。最后,通过在低维空间中保持样本间的相似性不变计算低维嵌入结果。相较于单一的图结构表示法,构造的全局–局部联合图充分利用了高维数据固有的全局和局部结构信息。此外,通过保持样本间的相似性能有效提取出高维轴承数据的本质特征,实验结果表明,提出的基于全局–局部图嵌入的特征提取方法较现有的方法具有明显优势。

The traditional graph-based fault diagnosis framework usually uses a certain structural relationship of high-dimensional dataset to construct a similarity graph to reveal the geometric structure between samples,resulting in the loss of other structural information of the dataset,and it is impossible to accurately extract the low-dimensional features that characterize the running state of the bearing.A new graph-based unsupervised feature extraction method is proposed,which considers both the global and local structures of high-dimensional dataset in the process of constructing graphs,which is called global-local graph embedding.The method first constructs an undirected graph by using the global structure information of the dataset.Then,by constructing local structure information and assigning corresponding weights to the edges in the undirected graph,a global-local graph joint representation convex optimization problem is obtained,and the similarity between samples is evaluated according to the obtained weights.Finally,the low-dimensional embedding result is calculated by keeping the similarity between samples unchanged in the low-dimensional space.Compared with the single graph structure representation,our constructed global-local joint graph takes full advantage of the global and local structural information inherent in high-dimensional dataset.In addition,the essential features of high-dimensional bearing data can be effectively extracted by maintaining the similar performance between samples.Experimental results show that our proposed feature extraction method based on global-local graph embedding has obvious advantages over existing methods.