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(EE-MD)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.
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.
出处
《东南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2011年第5期998-1001,共4页
Journal of Southeast University：Natural Science Edition
基金
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)
关键词
旋转机械
颤振
集合经验模式分解
奇异谱熵
rotating machinery
chatter
ensemble empirical mode decomposition
singular value spectral entropy
