[1]高清清,贾民平.基于EEMD的奇异谱熵在旋转机械故障诊断中的应用[J].东南大学学报(自然科学版),2011,41(5):998-1001.[doi:10.3969/j.issn.1001-0505.2011.05.020]
 Gao Qingqing,Jia Minping.EEMD method based singular value spectral entropy in fault diagnosis of rotating machinery[J].Journal of Southeast University (Natural Science Edition),2011,41(5):998-1001.[doi:10.3969/j.issn.1001-0505.2011.05.020]
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基于EEMD的奇异谱熵在旋转机械故障诊断中的应用()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
41
期数:
2011年第5期
页码:
998-1001
栏目:
机械工程
出版日期:
2011-09-20

文章信息/Info

Title:
EEMD method based singular value spectral entropy in fault diagnosis of rotating machinery
作者:
高清清贾民平
(东南大学机械工程学院,南京 211189)
Author(s):
Gao QingqingJia Minping
(School of Mechanical Engineering, Southeast University, Nanjing 211189,China)
关键词:
旋转机械颤振集合经验模式分解奇异谱熵
Keywords:
rotating machinery chatter ensemble empirical mode decomposition singular value spectral entropy
分类号:
TH17;TP206
DOI:
10.3969/j.issn.1001-0505.2011.05.020
摘要:
针对旋转机械振动信号的非平稳、非线性等特点,提出一种基于集合经验模式分解(EEMD)的奇异谱熵信号分析及故障诊断方法.该方法利用EEMD有效抑制模式混叠现象的优点,首先对原始振动信号进行EEMD分解,得到各阶本征模态函数(IMF),然后将各阶IMF分量构成一个特征模式矩阵,并对该特征模式矩阵求奇异谱熵值.奇异谱熵值的大小能够反映系统的工作状态和故障类型.分别用基于经验模式分解(EMD)和集合经验模式分解的奇异谱熵对车削颤振的振动信号分析对比,结果验证了该方法的有效性和可行性.
Abstract:
For the non-stationary and non-linear characteristics of rotating machinery vibration signal, ensemble empirical mode decomposition(EEMD) method based singular value spectral entropy is proposed for signal analysis and fault diagnosis of rotating machinery. This method utilizes the advantage of EEMD which can effectively restrain model mixing. First, the EEMD method is used to decompose the original signal to obtain intrinsic mode functions (IMFs). Then, a feature pattern matrix is created by intrinsic mode functions. Finally, the singular spectrum entropy of the feature pattern matrix is calculated, since singular spectrum entropy can reflect the system’s working condition and fault type. Singular value spectral entropy based on the EMD method and the EEMD method are respectively used to analyze and compare the turning chatter vibration signals. The result verifies that the proposed method is effective and feasible.

参考文献/References:

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备注/Memo

备注/Memo:
作者简介:高清清(1987—),女,硕士生;贾民平(联系人),男,博士,教授,博士生导师,mpjia@seu.edu.cn.
基金项目:国家自然科学基金资助项目(51075070,51075069)、国家高技术研究发展计划(863计划)资助项目(2007AA04Z421)、江苏省产学研联合创新资金资助项目(BY2009152).
引文格式: 高清清,贾民平.基于EEMD的奇异谱熵在旋转机械故障诊断中的应用[J].东南大学学报:自然科学版,2011,41(5):998-1001.[doi:10.3969/j.issn.1001-0505.2011.05.020]
更新日期/Last Update: 2011-09-20