版权说明 操作指南
首页 > 成果 > 成果详情

Enhanced symplectic characteristics mode decomposition method and its application in fault diagnosis of rolling bearing

认领
导出
Link by DOI
反馈
分享
QQ微信 微博
成果类型:
期刊论文
作者:
Cheng, Zhengyang;Wang, Rongji*
通讯作者:
Wang, Rongji
作者机构:
[Wang, Rongji; Cheng, Zhengyang] Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
通讯机构:
[Wang, Rongji] C
Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
语种:
英文
关键词:
Calculations;Eigenvalues and eigenfunctions;Roller bearings;Wavelet decomposition;Adaptive signal decomposition;Eigenvalue decomposition;Feature enhancement;Matrix decomposition;Mode decomposition method;Noise reduction methods;Nonstationary signals;Symplectic geometry;Signal processing
期刊:
Measurement
ISSN:
0263-2241
年:
2020
卷:
166
页码:
108108
机构署名:
本校为第一且通讯机构
院系归属:
机电工程学院
摘要:
As an adaptive signal decomposition method, symplectic geometry mode decomposition (SGMD) method is suitable for dealing with non-stationary signals However, the decomposition effect is not ideal when dealing with rolling bearing fault signals with strong background noise. On the one hand, this noise reduction method of SGMD is not suitable for fault signals with strong background noise. On the other hand, SGMD uses QR decomposition method, which results in decomposition error diffusion in the decomposition of singular matrix. Therefore, an enh...

反馈

验证码:
看不清楚,换一个
确定
取消

成果认领

标题:
用户 作者 通讯作者
请选择
请选择
确定
取消

提示

该栏目需要登录且有访问权限才可以访问

如果您有访问权限,请直接 登录访问

如果您没有访问权限,请联系管理员申请开通

管理员联系邮箱:yun@hnwdkj.com