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机译:机械中的数据对齐剩余使用深层对抗神经网络的使用寿命预测
Northeastern Univ Coll Sci Shenyang 110819 Peoples R China|Northeastern Univ Key Lab Vibrat & Control Aeroprop Syst Minist Educ Shenyang 110819 Peoples R China|Univ Cincinnati Dept Mech & Mat Engn Cincinnati OH 45221 USA;
Northeastern Univ Key Lab Vibrat & Control Aeroprop Syst Minist Educ Shenyang 110819 Peoples R China|Shenyang Aerosp Univ Sch Aerosp Engn Shenyang 110136 Peoples R China;
Northeastern Univ Key Lab Vibrat & Control Aeroprop Syst Minist Educ Shenyang 110819 Peoples R China|Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China;
Northeastern Univ Key Lab Vibrat & Control Aeroprop Syst Minist Educ Shenyang 110819 Peoples R China|Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China;
Northeastern Univ State Key Lab Rolling & Automat Shenyang 110819 Peoples R China;
Remaining useful life prediction; Rotating machines; Deep learning; Adversarial training; Data alignment;
机译:数据驱动剩余的使用寿命使用多个传感器信号和深长短期内存神经网络
机译:深度可分离卷积网络,用于预测机器的剩余使用寿命
机译:循环卷积神经网络:机械剩余使用寿命预测的新框架
机译:基于层次神经网络的旋转机械剩余使用寿命预测
机译:人工神经网络和自适应神经模糊推理系统模型预测水管剩余使用寿命
机译:基于AutoEncoder方案使用深卷积生成的对抗性网络剩余使用的生命估算
机译:顺序网络具有剩余机器旋转机器的剩余寿命预测使用深度转移学习