机译:基于小波自回归模型和主成分分析法的齿轮多故障诊断虚拟样机及实验研究
Reliability Engineering Institute, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China,Key Laboratory of Marine Power Engineering and Technology (Ministry of Transportation), Wuhan University of Technology, Wuhan 430063, China;
Reliability Engineering Institute, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China,Key Laboratory of Marine Power Engineering and Technology (Ministry of Transportation), Wuhan University of Technology, Wuhan 430063, China;
Reliability Engineering Institute, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China,Key Laboratory of Marine Power Engineering and Technology (Ministry of Transportation), Wuhan University of Technology, Wuhan 430063, China;
School of Engineering & Physical Sciences, James Cook University. Townsville, Qld. 4811, Australia;
Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang 443002, China;
gear fault diagnosis; virtual prototype; wavelet ar model; pca;
机译:经验模糊熵,主要成分分析和SOM神经网络模糊熵的多故障诊断滚动轴承
机译:基于主成分分析的传感器系统多故障诊断方法
机译:基于小波包变换的主成分分析和核主成分分析的齿轮故障特征提取与分类
机译:基于概率密度分布和主成分分析的智能诊断方法—在齿轮旋转机械上的应用
机译:主成分分析提高半导体故障检测与诊断可靠性的方法
机译:基于主成分分析的传感器系统多故障诊断方法
机译:使用实验方法的鲁棒NVH工程 - 元件转移路径分析和虚拟声学原型的源表征技术
机译:航空航天传感器组件和子系统调查与创新-2组件探索与开发(asCII-2 CED)。交付订单0002:第2卷。可重新配置的孔径天线虚拟原型(用于集成螺旋电感的宽带集总电路模型)