基于改进排列熵的滚动轴承故障特征提取  

Fault feature extraction of rolling bearings based on an improved permutation entropy

作　　者：陈祥龙 张兵志[2] 冯辅周 江鹏程 CHEN Xiang-long;ZHANG Bing-zhi;FENG Fu-zhou;JIANG Peng-cheng(Department of Ordnance Engineering,Sergeant Academy of PAP,Hangzhou 310023,China;Beijing Special Vehicle Research Institute,Beijing 100072,China;Department of Mechanical Engineering,Army Academy of Armored Forces,Beijing 100072,China)

机构地区：[1]武警士官学校军械系,浙江杭州310023 [2]北京特种车辆研究所,北京100072 [3]陆军装甲兵学院机械工程系,北京100072

出　　处：《振动工程学报》2018年第5期902-908,共7页Journal of Vibration Engineering

基　　金：装备预研基金重点项目(9140A27020115JB35071)。

摘　　要：排列熵能够有效监测振动信号中的动力学突变,衡量振动信号的复杂度,在旋转机械状态监测中获得成功的应用。将排列熵应用于滚动轴承故障特征提取中,并针对排列熵对振动信号幅值不敏感,无法反映振动信号中局部能量分布差异的问题,利用滤波信号的归一化瞬时能量改进排列熵,提出一种基于改进排列熵的滚动轴承故障特征提取方法。仿真和试验数据分析结果表明,该方法能够有效识别滚动轴承共振频带,准确提取滚动轴承故障特征。The permutation entropy can be used to efficiently detect the dynamic mutation and measure the complexity of 1-Dimensional time series.It has been successfully applied to condition detection of revolving machinery.This study proposes a method that applies permutation entropy into fault feature extraction of rolling bearings.However,permutation entropy is not sensitive to the amplitude of vibration signals and cannot reflect the difference of local energy distribution of vibration signals.Therefore,the permutation entropy is improved by utilizing the normalized instantaneous energy and a novel fault feature extraction method is proposed by the improved permutation entropy.The analysis results demonstrate that the proposed fault feature extraction method can effectively identity the resonant frequency band and extract fault features.

关 键 词：故障诊断 滚动轴承 排列熵 特征提取 

分 类 号：TH165.3[机械工程—机械制造及自动化] TH133.33