摘要
支持向量数据描述(SVDD)是一种单值分类方法。该方法能够在只有一类学习样本的情况下建立分类器,在机械故障诊断中的应用有望解决制约智能故障诊断技术发展的故障数据缺乏问题。提出了一种基于支持向量数据描述的故障诊断方法,利用非目标样本即故障数据,来提高故障诊断准确率的方法。仿真实验结果显示,该方法可有效利用故障数据提高故障诊断的准确性。
Support vector data description （ SVDD ） is a one-class classification method. It can build a classifier with only one class of learning sample. Thus, application of SVDD in mechanical fault diagnosis is expected to solve the problem of shortage of fault data in development of intelligent fault diagnosis technology. The method based on support vector data description for fault diagnosis is presented. By adopting outlier object sample, the accuracy of fault diagnosis is enhanced. The result of simulation experiment shows that the method can improve the precision of diagnosis significantly.
出处
《自动化仪表》
CAS
2008年第6期12-14,共3页
Process Automation Instrumentation
关键词
支持向量数据描述
故障诊断
非目标样本数据
智能诊断
单分类
Support vector data description Fault diagnosis Outlier object sample data Intelligent diagnosis One-class classification
