一种参数自适应VMD应用于轴承故障特征提取
A Parameter Adaptive VMD Applied to Bearing Fault Feature Extraction
高淑芝 1陈雪峰 2张义民1
作者信息
- 1. 沈阳化工大学装备可靠性研究所,辽宁 沈阳 110142
- 2. 沈阳化工大学信息工程学院,辽宁 沈阳 110142
- 折叠
摘要
针对传统的变分模态分解(VMD)需要预先设置模态个数和惩罚参数,提出了一种基于麻雀搜索算法(SSA)的参数自适应VMD方法.首先,引入一种新的测量指标—相关脉冲,该指标能反映出原始信号与分解模态之间的相关性,并且能有效突出包含丰富信息的模态.其次,基于相关脉冲指标,采用麻雀搜索算法选择最优VMD分解参数.最后,通过最大相关脉冲指标对模态分量进行分析,利用希尔伯特包络谱进行频谱分析.此外,将故障轴承放在轴承寿命试验台上进行仿真验证,实验结果表明该方法在轴承故障特征提取上具有可行性.
Abstract
In view of the fact that the traditional variational mode decomposition(VMD)needs to set the number of modes and penalty pa-rameters in advance,a parameter adaptive VMD method based on sparrow algorithm(SSA)is proposed.Firstly,a new measurement in-dex correlation pulse is introduced,which can reflect the correlation between the original signal and the decomposed mode,and can effec-tively highlight the mode with rich information.Secondly,based on the correlation pulse index,the sparrow search algorithm is used to select the optimal VMD decomposition parameters.Finally,the mode components are analyzed by the maximum correlation pulse index,and the Hilbert envelope spectrum is used to spectrum analysis.In addition,the faulty bearing is placed on the bearing life test bench for simulation verification.The experimental results show that the method is feasible in the extraction of bearing fault features.
关键词
变分模态分解/麻雀搜索算法/相关脉冲/故障特征提取Key words
Variational Mode Decomposition/Sparrow Search Algorithm/Correlation Pulse/Fault Feature Extraction引用本文复制引用
基金项目
国家自然科学基金-辽宁联合基金重点项目(U1708254)
辽宁省特聘教授项目([2018]3533)
出版年
2024
NETL
NSTL
万方数据