机译:基于特征选择反馈网络的轴承故障诊断方法及改进的D-S证据融合
Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Peoples R China|Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Peoples R China|Guizhou Univ Sch Mech Engn Guiyang 550025 Peoples R China;
Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Peoples R China;
Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Peoples R China;
Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Peoples R China;
Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Peoples R China;
Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Peoples R China|Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Peoples R China|Guizhou Univ Sch Mech Engn Guiyang 550025 Peoples R China;
Bearing fault diagnosis; feature selection; feedback network; D-S evidence theory;
机译:基于GLCM,选择方法的融合,融合,改进了轴承故障诊断,选择方法,以及多标菌-NAï ve Bayes分类
机译:基于加权改进D-S证据理论的地铁车辆转向架轴承智能故障诊断方法
机译:基于D-S证据理论的机器故障诊断。第2部分:改进的D-S证据理论在变速箱故障诊断中的应用
机译:基于加权D-S证据理论的多传感器融合轴承故障诊断
机译:使用D-S理论的证据融合:在无线网络中利用不断发展的可靠性因素。
机译:具有改进的D-S证据融合的集成深度卷积神经网络模型用于轴承故障诊断
机译:基于特征选择反馈网络的轴承故障诊断方法及改进的D-S证据融合