融合邻域分布 LLE 算法轴承故障信号检测

张彦生^a,b , 张利来 ^a,b , 刘远红 ^a,b

收稿日期:2022-10-29 出版日期:2023-10-09 发布日期:2023-10-10
作者简介:张彦生(1980— ), 女, 黑龙江大庆人, 东北石油大学副教授, 硕士生导师, 主要从事流形学习研究, (Tel)86-13349508007(E-mail)zhangyansheng@ nepu. edu. cn。

ZHANG Yansheng^a,b , ZHANG Lilai ^a,b , LIU Yuanhong ^a,b

a. School of Electrical and Information Engineering; b. Northeast Petroleum University National Science Park, Northeast Petroleum University, Daqing 163318, China

摘要：

针对降维算法局部线性嵌入算法 LLE(Local Linear Embedding)未能充分保留高维数据中邻域之间的结构

的问题, 提出了一种新的融合邻域分布属性的局部线性嵌入算法。该算法通过计算每个样本数据的邻域分布

以及 KL(Kullback-Leibler)散度度量不同邻域点与其中心样本各自的近邻分布差异, 并利用其差值优化重构的

权重系数, 从而获得更精确的低维电机数据。通过可视化、 Fisher 测量和识别精度 3 个评价结果验证了该算法

挖掘电机轴承检测数据高维结构的有效性。

关键词: 局部线性嵌入, 邻域分布, 降维算法, 电机轴承

Abstract:

For the problem that LLE(Local Linear Embedding) fails to adequately preserve the structure between

neighborhoods in high-dimensional data, a new local linear embedding algorithm is proposed for fused

neighborhood distribution properties. The algorithm calculates the neighborhood distribution of each sample data,

then calculates the respective nearest neighborhood distribution difference of the KL ( Kullback-Leibler)

divergence measure between the different neighborhood point and its central sample, and finally optimizes the

reconstructed weight coefficient to obtain more accurate low-dimensional motor data. The effectiveness of the

algorithm is verified by three evaluations of visualization, Fisher measurement and identification accuracy.

Key words: local linear embedding, neighborhood distribution, dimension reduction algorithm, motor bearing

中图分类号:

张彦生, 张利来, 刘远红 .

融合邻域分布 LLE 算法轴承故障信号检测 [J]. 吉林大学学报(信息科学版), 2023, 41(5): 780-786.

ZHANG Yansheng , ZHANG Lilai , LIU Yuanhong .

Bearing Signal Detection for the Fusion Neighborhood

Distribution of LLE Algorithm [J]. Journal of Jilin University (Information Science Edition), 2023, 41(5): 780-786.

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