摘要
提出一种基于小波特征提取和支持向量数据描述的故障智能诊断方法,通过提取实测信号经小波分解后各频带重构信号的能量作为特征,进行支持向量数据描述分类器的训练和分类。通过对滚动轴承故障智能诊断实例表明,该方法可以有效提取信号的故障特征,改进支持向量数据描述在故障诊断中精确度。
A novel method based on wavelet and SVDD was proposed, and it was applied to rolling bearings fault diagnosis. The results show that the presented method is efficient to extract the fault feature, reduce the dimension of the signals and improve the veracity of one-class classification in intelligent diagnosis significantly.
出处
《科学技术与工程》
2009年第21期6374-6378,共5页
Science Technology and Engineering
关键词
支持向量数据描述
小波分解
单值分类
故障诊断
support vector data description
wavelet decomposition
one-class classification
fault diagnosis
