基于小波包样本熵的滚动轴承故障特征提取  

Feature Extraction of Rolling Element Bearing Fault Using Wavelet Packet Sample Entropy

作　　者：苏文胜[1,2] 王奉涛[1] 朱泓[1] 郭正刚[1] 张志新[1] 张洪印 

机构地区：[1]大连理工大学机械学院,大连116024 [2]江苏省特种设备安全监督检验研究院无锡分院,无锡214171 [3]中国石油长城钻探工程公司固井公司,盘锦124010

出　　处：《振动．测试与诊断》2011年第2期162-166,263,共5页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(编号:50805014);教育部科学技术研究重点资助项目(编号:109047)

摘　　要：将样本熵引入故障诊断领域,讨论了样本熵的性能和计算参数的选择。结合小波包分解和样本熵,提出了一种新的滚动轴承故障特征提取方法。首先对轴承振动信号进行小波包分解;然后对归一化能量最大的子带进行重构,计算重构信号的样本熵;最后通过样本熵评价故障状态。滚动轴承故障诊断实例验证了该方法的有效性。Fault diagnosis of rolling element bearing is important to improve the performance and the reliability of mechanical systems.The extraction of feature parameters is essential to diagnose faults.The sample entropy was introduced into the field of fault diagnosis.Its performance and the choice of calculation parameters were discussed.Combined with wavelet packet decomposition and sample entropy,a feature extraction method for rolling element bearing faults was proposed.Firstly,the bearing vibration signal was processed with wavelet packet decomposition.Then,the sub-band with largest normalized energy was reconstructed.Finally, the sample entropy of the reconstructed signal was calculated and used to evaluate the fault condition.The practical application proves that the method is effective on fault diagnosis of rolling element bearing.

关 键 词：小波包分解 样本熵 滚动轴承 故障诊断 

分 类 号：TH133.3[机械工程—机械制造及自动化]