基于LMD多尺度样本熵和GG聚类的滚动轴承故障辨识方法

兰州理工大学学报 ›› 2026, Vol. 52 ›› Issue (1): 56-62.

基于LMD多尺度样本熵和GG聚类的滚动轴承故障辨识方法

袁瑞博¹, 赵荣珍^*1,2, 邓林峰¹

1.兰州理工大学机电工程学院, 甘肃兰州 730050;
2.广州理工学院智能制造与电气工程学院, 广东广州 510540

收稿日期:2023-02-18 出版日期:2026-02-28 发布日期:2026-03-05
通讯作者: 赵荣珍(1960-),女,山东枣庄人,博士,教授,博导.Email:zhaorongzhen@lut.edu.cn
基金资助:
国家自然科学基金(62241308,51675253)

Rolling bearing fault identification method based on LMD multi-scale sample entropy and GG clustering

YUAN Rui-bo¹, ZHAO Rong-zhen^1,2, DENG Lin-feng¹

1. School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China;
2. College of Intelligent Manufacturing and Electrical Engineering, Guangzhou Institute of Science and Technology, Guangzhou 510540, China

Received:2023-02-18 Online:2026-02-28 Published:2026-03-05

摘要/Abstract

摘要： 针对滚动轴承故障信号因非线性、非平稳性而导致故障类别难以辨识的问题,提出了基于局部均值分解(LMD)、多尺度样本熵与GG聚类方法相结合的故障辨识方法.该方法首先采用LMD对滚动轴承的故障信号进行分解,得到多个乘积函数(PF)分量,初步提取滚动轴承的状态特征;其次,通过相关性分析选出最优PF分量,并在多个尺度下计算样本熵;最后,运用主成分分析对高维特征向量进行可视化降维,并输入GG聚类方法实现滚动轴承故障类别的辨识.结果表明,该方法相比其他模式组合的方法聚类效果更优.

关键词: 局部均值分解, 多尺度样本熵, 相关性分析, GG聚类

Abstract: Aiming at the problem of difficulty in identifying the fault category caused by the nonlinear and non-smooth characteristics of rolling bearing fault signal, a fault identification method based on the combination of Local Mean Decomposition (LMD), multi-scale sample entropy and GG clustering algorithm is proposed. Initially, the fault signals of rolling bearings are decomposed using LMD to obtain multiple product function (PF) components, enabling the preliminary extraction of rolling bearing state features. Then, the optimal PF components are selected by correlation analysis, and the sample entropy value are computed at multiple scales. Finally, the principal component analysis is employed to visualize and reduce the dimensionality of the high-dimensional feature vectors. The new feature vectors are visualized by principal component analysis, which are then input into the GG clustering algorithm to realize the identification of rolling bearing fault categories. The results show that the proposed method has the advantage of better clustering effect compared with other pattern combination methods.

Key words: local mean decomposition, multiscale sample entropy, correlation analysis, GG clustering

中图分类号:

TH133
TH165

袁瑞博, 赵荣珍, 邓林峰. 基于LMD多尺度样本熵和GG聚类的滚动轴承故障辨识方法[J]. 兰州理工大学学报, 2026, 52(1): 56-62.

YUAN Rui-bo, ZHAO Rong-zhen, DENG Lin-feng. Rolling bearing fault identification method based on LMD multi-scale sample entropy and GG clustering[J]. Journal of Lanzhou University of Technology, 2026, 52(1): 56-62.

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