支持向量数据描述和经验模态分解相结合的故障诊断  

FAULT DIAGNOSIS COMBINED SUPPORT VECTOR DATA DESCRIPTION AND EMPIRICAL MODE DECOMPOSITION

作　　者：王昆[1] 李凌均[1] 周喜格[1] 

机构地区：[1]郑州大学振动工程研究所,郑州450001

出　　处：《机械强度》2009年第6期1012-1014,共3页Journal of Mechanical Strength

基　　金：国家自然科学基金(50675209);河南省自然科学基金(0611022400);河南省杰出人才创新基金(0621000500)资助项目~~

摘　　要：为了解决智能监测和故障诊断中故障样本缺乏的问题,提出一种支持向量数据描述(support vector data description,SVDD)和经验模态分解(empirical mode decomposition,EMD)相结合的单分类方法。该方法在只有正常状态数据样本而无需故障样本的情况下可以建立起单值分类器,从而区分出机器的运行状态。采用经验模态分解对数据进行预处理,提取信号在不同频带的能量特征作为SVDD的输入参数进行分类。将该方法应用于滚动轴承的故障诊断中,试验结果表明,该方法可以比传统的SVDD方法更有效地识别轴承的运行状态。In order to solve the problem of insufficient fault samples in intelligent monitoring and fault diagnosis, a one-class classification method combined support vector data description （SVDD） and empirical mode decomposition （EMD） is proposed. With this method, one-class classifier can be built when only the object of normal condition is available, and the abnormal condition can be distinguished from normal condition. Empirical mode decomposition （EMD） is used as data preprocessing to extract the energy varies in different frequencies bands, and the energy features extracted by EMD can be served as the input parameters of SVDD for classification. Applying this method to the wiling bearing fault diagnosis, the test result shows that this method is superior to traditional SVDD method and would identify roiling bearing fault patterns more effectively.

关 键 词：支持向量数据描述 单值分类 故障诊断 经验模态分解 

分 类 号：TH165.3[机械工程—机械制造及自动化] TP206.3[自动化与计算机技术—检测技术与自动化装置]