基于lmd样本熵和LS-SVM的滚动轴承故障分类

Jie Bian, Changqin Huo, Guang Tang, Jun Gao, Lisheng Lin
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引用次数: 1

摘要

针对滚动轴承振动信号的非线性和非平稳特征,提出了一种基于局部均值分解(LMD)-样本熵和最小二乘支持向量机(LS-SVM)的滚动轴承故障分类方法。采用LMD方法将滚动轴承振动信号分解为多个积函数分量,选取包含主要故障信息的前几个积函数分量的样本熵作为特征向量。然后,利用LS-SVM对正常轴承、内滚圈故障轴承和外滚圈故障轴承的振动信号进行分析和识别。结果表明,本文提出的方法可以有效地对滚动轴承的各种状态进行分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault classification of rolling bearing based on LMD-sample entropy and LS-SVM
In view of the nonlinear and non-stationary characteristics of vibration signals for rolling bearings, a fault classification method of rolling bearing based on local mean decomposition (LMD)-sample entropy and Least Squares Support Vector Machines (LS-SVM) was proposed. LMD method was employed to decompose vibration signals of rolling bearings into several product function components, and sample entropy of the first few PF components containing main fault information was selected as the characteristic vectors. Then, LS-SVM was used to analyze and identify vibration signals of normal bearing, inner race faulty bearing and outer race faulty bearing. The results show that the method proposed in the paper can classify various states of rolling bearings effectively.
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