基于边界判别多流形分析的故障数据集降维方法

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振动与冲击 ›› 2021, Vol. 40 ›› Issue (23) : 120-126.

论文

常书源1,2，赵荣珍1，陈博1，何天经1，石明宽1

作者信息 +

Dimension reduction method of fault data set based on BDMA

CHANG Shuyuan1,2, ZHAO Rongzhen1, CHEN Bo1, HE Tianjing1, SHI Mingkuan1

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文章历史 +

摘要

针对现有多流形学习方法未考虑流形间边界信息而导致降维后数据不易于分类的问题，提出一种新的边界判别多流形分析(Margin Discriminant Multi-manifold Analysis, MDMA)方法。该方法同时考虑数据的类内相似性、类间差异性、同类流形结构和异类流形结构，并且为避免降维过程中出现小样本问题，在构造目标函数时将这四点归结为指数化迹商优化结构。通过两个转子系统实验数据集进行验证，结果表明，与其他几种典型降维方法对比，该方法能更有效地提取出蕴含在数据中的判别信息，在故障辨识中表现出更好的分类性能。

Abstract

Aiming at the problem that the existing multi-manifold learning method does not consider the boundary information between the manifolds, which leads to the difficulty of data classification after dimensionality reduction, a new margin discriminant multi-manifold analysis (MDMA) method was proposed. In the MDMA, the intra-class similarity, inter-class difference, congeneric manifold structure and heterogeneous manifold structure of the data were considered at the same time, and in order to avoid the problem of small sample in the process of dimensionality reduction, the four points were summed up as the exponential trace quotient optimization structure when constructing the objective function. The experimental data set of two rotor systems was verified, and the results showed that compared with other typical dimensionality reduction methods, the method could extract the discriminant information contained in the data more effectively, and showed better classification performance in fault identification.

导出引用

常书源1,2，赵荣珍1，陈博1，何天经1，石明宽1. 基于边界判别多流形分析的故障数据集降维方法[J]. 振动与冲击, 2021, 40(23): 120-126

CHANG Shuyuan1,2, ZHAO Rongzhen1, CHEN Bo1, HE Tianjing1, SHI Mingkuan1. Dimension reduction method of fault data set based on BDMA[J]. Journal of Vibration and Shock, 2021, 40(23): 120-126

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