一种基于SLS_SVM的滚动轴承故障诊断方法OA
A Fault Identification Method for Rolling Bearing Based on SLS_SVM
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。
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 fanlt identification method for roiling bearing is proposed. A comparison of this method with the standard S…查看全部>>
柴美娟;柳桂国
浙江工商职业技术学院设备与设备管理办公室,浙江宁波315012浙江工商职业技术学院设备与设备管理办公室,浙江宁波315012
机械制造
滚动轴承最小二乘支持向量机半监督学习故障诊断
rolling bearingLS_SVMsemi-supervised learningfault identification
《电子科技》 2012 (6)
136-139,4
浙江省科技计划基金资助项目（2009C31105）