一种基于SLS_SVM的滚动轴承故障诊断方法

为提高滚动轴承故障诊断分类器的训练正确率,以及缩短训练时间,根据其训练集即含有标签样本,也含有无标签样本的特点,将LS_SVM与半监督学习相结合,充分利用训练集中的有效信息,给出一种基于SLS_SVM的滚动轴承故障诊断方法。将该方法与标准SVM和半监督学习SVM方法相比,其不但能提高训练正确率,也能缩短训练所需时间。通过诊断试验,验证了该算法的有效性以及高效性。

A Fault Identification Method for Rolling Bearing Based on SLS_SVM

To improve the rate of correct training of the fault identification sorter of rolling bearing and shorten the training time,LS_SVM is combined with semi-supervised learning according to the fact that the training sets have both labeled and unlabeled examples.Full use is made of the effective information in the training sets and a novel SLS_SVM based fault identification method for rolling bearing is proposed.A comparison of this method with the standard SVM and semi-supervised learning based on the SVM method shows that this method can not only improve the rate of correct training but also shorten the training time.The diagnostic tests show that it is an effective and efficient method.