机译:具有新训练方法的深度卷积神经网络,用于嘈杂环境和不同工作负荷下的轴承故障诊断
State Key Laboratory of Robotics and System, Harbin Institute of Technology, No. 92 Xidazhi Street, Harbin 150001, Heilongjiang Province, China;
State Key Laboratory of Robotics and System, Harbin Institute of Technology, No. 92 Xidazhi Street, Harbin 150001, Heilongjiang Province, China;
State Key Laboratory of Robotics and System, Harbin Institute of Technology, No. 92 Xidazhi Street, Harbin 150001, Heilongjiang Province, China;
State Key Laboratory of Robotics and System, Harbin Institute of Technology, No. 92 Xidazhi Street, Harbin 150001, Heilongjiang Province, China;
State Key Laboratory of Robotics and System, Harbin Institute of Technology, No. 92 Xidazhi Street, Harbin 150001, Heilongjiang Province, China;
Intelligent fault diagnosis; Convolutional neural networks; Load domain adaptation; Anti-noise; End-to-end;
机译:不同工作条件下的轴承故障诊断的新型自适应和快速深度卷积神经网络
机译:基于循环谱相干和卷积神经网络的轴承故障诊断深度学习方法
机译:深度多尺度卷积转移学习网络:可变工作条件与域下滚动轴承智能故障诊断的新方法
机译:噪声环境下结构优化的深度卷积神经网络轴承故障诊断
机译:流水线训练与深卷积神经网络的陈旧重量
机译:基于集成卷积神经网络和深度神经网络的特征融合方法进行轴承故障诊断
机译:深度噪声下滚动轴承故障诊断的深度卷积和LSTM经常性神经网络