机译:基于Cluster-MWMote和MFO优化LS-SVM的新的IMbalanced故障诊断框架使用有限和复杂的轴承数据
Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guiyang Guizhou 550025 China;
Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guiyang Guizhou 550025 China;
Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guiyang Guizhou 550025 China Department of Industrial Engineering and Management Yuan Ze University Taoyuan 32003 Taiwan;
Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guiyang Guizhou 550025 China Guizhou Renhe Zhiyuan Data Service Co. Ltd. Guiyang Guizhou 550025 China;
Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guiyang Guizhou 550025 China;
Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guiyang Guizhou 550025 China;
Cluster-MWMOTE; LS-SVM; Complex imbalanced classification; Hyperparameter optimization; Bearing fault diagnosis;
机译:基于GSA-WELM和GSA-ELM的滚动元件轴承的两步故障诊断框架
机译:考虑数据不平衡和可变工作条件,基于归一化CNN的滚动轴承智能故障诊断
机译:基于小波能熵和LS-SVM的滚动轴承的状态监测及早期故障诊断
机译:数据不平衡的滚动轴承的两步故障诊断框架
机译:使用自组织地图的Imbalanced和Sparars标记的数据集故障检测框架
机译:可变条件下针对不平衡未标记数据的轴承故障诊断的半监督方法
机译:基于不平衡数据集的参数转移学习的滚动轴承故障诊断方法