基于EEMD能量矩与邻域粗糙集的转子故障数据集分类方法

针对旋转机械故障辨识准确率偏低的问题，将经验模态分解（ensemble empirical mode decomposition，EEMD）与能量矩、邻域粗糙集（neighborhood rough set，NRS）结合提出一种转子系统故障模式辨识的方法。首先利用EEMD将采集到的振动故障信号自适应分解成若干个平稳的本征模态函数（intrinsic mode function，IMF）分量并计算其能量矩；以此能量矩作为描述故障状态的条件属性建立故障识别决策表；然后利用邻域粗糙集对决策表进行属性约简消除冗余的属性；最后将约简后的敏感特征子集输入所设计的决策树（decision tree，DT）C4.5 算法中进行模式识别。通过典型转子实验台的故障特征集验证了该方法的有效性。

Rotor fault data set classification method based on EEMD energy moment and neighborhood rough sets

Aiming at the problem of low recognition accuracy of rotating machinery faults, this paper proposed a rotor system fault identification method based on the combination of empirical mode decomposition(EEMD) with energy moment and neighborhood rough sets(NRS). Firstly, this method used the EEMD to decompose the non-stationary vibration signal into several sTable intrinsic mode functions(IMF) and calculated the energy moment of the IMF component. This energy moment was used as the condition attribute to describe the fault state to establish the fault identification decision table. Then this paper used the neighborhood rough set to perform attribute reduction on the decision table to eliminate the redundant attribute. Finally, it used the reduced sensitivity feature subsets as input into decision tree C4.5 algorithm for recognition. The experimental results of fault feature set verify effectiveness of this method.