信息熵与经验模态分解集成的转子故障信号量化特征提取

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Aimed at the nonlinearity and non-stationary of rotor vibration signal as well as the difficulty of its quantitative and accurate description,a method of quantitative feature extraction of rotor vibration signal was presented based on information entropy and empirical mode decomposition（EMD）.In this method were contrasted the energy state and its correlation degree to original signal of all intrinsic mode function（IMF） after EMD of the rotor fault signal and the IMFs with key fault information were determined.Then the entropy values of the four information entropy were evaluated in the time domain,frequency domain and time-frequency domain respectively,so that a feature quantity of information entropy was set up.The analysis result of experimental signal showed that this method could be used to realize well the extraction of quantitative feature of rotor system fault signal.The set of extracted feature would have the capability to makeremarkabe difference between the feature quantities of the typical fault signal.针对转子振动信号的非线性、非平稳性造成的故障状态难以定量准确描述问题,提出一种基于信息熵和经验模态分解（empiricalmodedecomposition,EMD）的转子振动信号量化特征提取方法.该方法通过对比转子故障信号EMD分解后各内禀模态分量（intrinsicmodefunction,IMF）的能量状态及其与原始信号间的相关性程度,在确定出包含主要故障信息的分量基础上,分别对其进行时域、频域及时频域内4种信息熵熵值的计算,从而建立起一种信息熵熵带特征量.实验信号的分析结果表明,该方法能够较好地实现对转子系统故障信号的量化特征提取,所提取出的特征集合具有能够使典型故障特征量之间存在显著差异的性能.