摘要
火车故障的60%都是由于滚轴问题引起的,现在的诊断方法都是基于知识的,但故障样本的不足从一定程度上制约了基于知识的方法在实际中的应用,针对这一问题,利用支持向量机在小样本情况下具有较强分类能力的特点,本文提出了一种基于支持向量机的滚轴故障诊断方法。该方法采用小波变换对齿轮的震动信号进行处理来构造特征向量,然后输入到支持向量机分类器中进行模式识别。
The 60% fault of train is attributed to bearing problem. The method used for fault diagnosis is based on knowledge now. But lack of fault samples restricts the application of the methods based on knowledge in practical fault diagnosis to a certain extent. In order to solve this problem, a diagnosis method of bearing fault based on a support vector machine was proposed based on the advantage that a support vector machine has strong classification ability with fewer samples. According to this method, feature vectors were extracted from bearing vibration signals after wavelet transform and they were input into amultiole-fault classifier of the support vector machine for fault identification.
出处
《机床与液压》
北大核心
2007年第7期248-250,共3页
Machine Tool & Hydraulics
关键词
滚轴
支持向量机
小波变换
故障诊断
Bearing
Support vector machine
Wavelet transform
Fault diagnosis
