基于超图相关距离判别投影的轴承故障诊断方法

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摘要针对滚动轴承故障数据维度过高以及不同特征属性交错导致的故障分类困难，提出了一种基于超图相关距离判别投影（Hypergraph Correlation Distance Discriminant Projection，HCDDP）的轴承故障数据降维方法。该方法使用超图结构描述了故障样本间的空间结构关系，并利用轴承故障信号的监督信息构建出类内和类间超图。超图更加有效的揭示了故障数据的复杂多重结构，相比传统简单图结构更好的表达了故障样本间的内在性质和多元关系。同时，在超图中提出使用皮尔森相关系数构造了一种新的度量来计算高维流形中样本的测地距离，解决了欧氏距离受故障数据取值范围敏感导致的分类不准确问题。超图相关距离判别投影方法具有的非线性数据高阶关联能力更好的解决了轴承故障的分类代价敏感。本方法在美国凯斯西储大学轴承数据集和西安交通大学轴承数据集上进行了验证。实验结果表明，本方法能够有效利用样本间的多元结构关系和判别信息，提高轴承故障的识别率。

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	苏树智
	张志鹏

关键词 ：故障诊断, 滚动轴承, 超图学习, 维数约简

Abstract：Aiming at the difficulty of fault classification caused by the high dimension of rolling bearing fault data and the interleaving of different feature attributes, a dimension reduction method of bearing fault data based on Hypergraph Correlation Distance Discriminant Projection(HCDDP) was proposed. This method uses hypergraph structure to describe the spatial structure relationship between fault samples, and uses the supervision information of bearing fault signals to construct intra-class and inter-class hypergraphs. The hypergraph more effectively reveals the complex multiple structure of fault data, and better expresses the intrinsic nature and multivariate relationship between fault samples than the traditional simple graph structure. At the same time, Pearson correlation coefficient is used to construct a new metric in the hypergraph to calculate the geodesic distance of samples in the high-dimensional manifold, which solves the problem of inaccurate classification caused by the sensitivity of Euclidean distance to the value range of fault data. The hypergraph correlation distance discriminant projection method has the ability of high-order correlation of nonlinear data to better solve the cost-sensitive classification of bearing faults. This method is verified on the bearing dataset of Case Western Reserve University and Xi 'an Jiaotong University in the United States. Experimental results show that the proposed method can effectively use the multivariate structural relationship and discriminant information between samples, and improve the recognition rate of bearing faults.

Key words： fault diagnosis rolling bearing graph learning dimension reduction

收稿日期: 2022-10-09 出版日期: 2023-12-15

引用本文:

苏树智，张志鹏. 基于超图相关距离判别投影的轴承故障诊断方法[J]. 振动与冲击, 2023, 42(23): 103-111.
SU Shuzhi,ZHANG Zhipeng. Bearing fault diagnosis method based on HCDDP. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(23): 103-111.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I23/103

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