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deep-learning-fault-diagnosis's Introduction

deep learning fault diagnosis

高被引论文模型复现

可选研究方向

  • 域适应问题：某一工况下训练，提高其在另一工况下的诊断精度。模型的泛化能力。由于工作任务的变化，机器工作负载也会随之改变，如何利用在一个负载下的数据进行训练，对另一个负载下的信号进行诊断。
  • 抗噪声：提高模型抗噪声能力，工业现场的噪声无法避免，使用加速度计测得的振动信号易被污染，研究如何从含有噪声的信号中诊断出轴承的故障
  • 类别不平衡问题：健康数据>>故障数据
  • 能不能找到新的损失函数，不是Pointwise，考虑之前状态？

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