正交小波变换支持向量数据描述在轴承性能评估中的应用  

Applying Orthogonal Wavelet Transform-SVDD to Evaluating Performance of Bearing

作　　者：李凌均[1] 韩捷[1] 李卫鹏[1] 郝伟[1] 

机构地区：[1]郑州大学机械工程学院,郑州450001

出　　处：《机械科学与技术》2012年第7期1201-1204,共4页Mechanical Science and Technology for Aerospace Engineering

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

摘　　要：支持向量数据描述是一种单值分类方法,该方法能够在缺少故障样本的情况下,仅仅利用采集到的正常状态数据样本建立起单值分类器,从而区分出机器的运行状态。正交小波变换对提取非平稳信号的冲击成分具有良好的性能。提出了一种基于正交小波变换和支持向量数据描述的状态评估方法,利用正交小波变换方法提取各细节信号的峰峰值作为分类器的输入参数,用支持向量数据描述方法建立起分类模型对机器运行状态进行定量评估。对滚动轴承内圈不同程度的点蚀故障进行了试验分析,建立起了对滚动轴承性能退化程度评估的定量指标。The support vector data description （SVDD） is a kind of single-value classification method, by which a single-value classifier can be built by using its normal state data samples even if the fault samples are lacking, thus revealing its normal operation. The orthogonal wavelet transform （OWT） has good performance for extracting the shock elements of a non-stable signal. We propose a new state evaluation method that uses the SVDD and the OWT and use the OWT to extract the peak-peak values of various detail signals, which are in turn used as input parame- ters of the classifier. We build the classification model of the classifier with the SVDD method to carry out the quan- titive evaluation of the state of the machine. We also use our method to do experimental analysis of the pitting faults on the inner ring of a rolling bearing and establish the quantitive indicators for evaluating its worsening perform- ance; the experimental results show that our method is effective.

关 键 词：支持向量数据描述 故障诊断 性能评估 正交小波变换 

分 类 号：TH165.3[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]