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基于ISGMD和MED的齿轮箱早期故障特征提取
Authors
杨世明
秦训鹏
董书洲
Publication date
1 January 2022
Publisher
Editorial Office of Journal of Mechanical Transmission
Doi
Cite
Abstract
针对在强噪声背景下难以识别齿轮箱早期故障以及复合故障的问题,提出了一种改进辛几何模态分解(Improved symplectic geometry mode decomposition,ISGMD)和最小熵解卷积(Minimum entropy deconvolution,MED)相结合的故障特征提取方法。首先,将信号经最小熵解卷积预处理,突出信号中的故障冲击成分;然后,将故障增强信号通过改进辛几何模态分解自适应地分解为若干辛几何分量,并依据峭度最大准则选取峭度值最大的敏感辛几何分量;最后,对选定的敏感辛几何分量进行包络分析,从而有效地提取出齿轮箱的故障特征。通过实验,验证了该方法的有效性
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Last time updated on 06/04/2023