基于经验模态分解的行星齿轮箱故障特征提取新方法
New method for fault feature extraction of rotating machinery based on empirical mode decomposition
王付广1
作者信息
- 1. 铜陵学院实践教学管理处,安徽 铜陵 244000
- 折叠
摘要
行星齿轮箱有着应用广泛、结构紧凑、适应恶劣工况的特点,其振动信号中包含强背景噪声以及大量啮合振动耦合,因此行星齿轮箱振动信号故障特征提取困难.针对上述情况,基于经验模态分解(empirical mode decomposition,EMD)、多尺度模糊熵以及波形指标提出了一种新的行星齿轮箱故障特征提取方法.首先,通过EMD将原始信号分解为单分量信号(IMF);其次,筛选IMF进行重构;最后,利用多尺度模糊熵和波形指标进行参数融合.试验结果表明:该方法可有效区分行星齿轮箱不同故障类型.
Abstract
The fault feature extraction of the vibration signal of the planetary gearbox is difficult because of its wide application,compact structure and bad working conditions,and its vibration signal contains strong background noise and a lot of meshing vi-bration coupling.In view of the above,based on empirical mode decomposition(EMD),multi-scale fuzzy entropy,and waveform index,a new fault feature extraction method for planetary gearbox is proposed in this paper.First,the original signal is decom-posed into a single component signal(IMF)by EMD.Then,the IMF is filtered and reconstructed.Finally,multi-scale fuzzy en-tropy and waveform index are used for parameter fusion.The experimental results show that the proposed method can effectively distinguish different fault types of planetary gearboxs.
关键词
经验模态分解/多尺度模糊熵/波形指标/行星齿轮箱Key words
empirical mode decomposition/multi-scale fuzzy entropy/waveform index/planetary gearbox引用本文复制引用
基金项目
铜陵学院校级科研项目(2022tlxy49)
出版年
2024
NETL
NSTL
维普
万方数据