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基于注意力机制的数控机床进给轴深度学习故障诊断
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重庆理工大学研究生科研创新项目(clgycx20202082;clgycx20202073)


Depth Learning Fault Diagnosis of NC Machine Tool Feed Axis Based on Attention Mechanism
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    摘要:

    针对传统故障诊断方法易受振动传感器安装位置的影响、故障诊断准确性不高的问题,提出一种基于注意力机制的数控机床进给轴深度学习故障诊断方法(AM-CNN-GRU)。该方法以数控机床进给轴的电流、电压与温度等相关数据作为输入数据,针对采集的进给轴数据中蕴含大量的时空特征信息,为了提取数据中的时空信息以提高故障诊断准确性,设计一种由CNN与GRU并联组成的时空特征提取结构。为验证所提方法的准确性,利用FANUC数控提供的FOCAS数据开发包编写数控机床实时监测与数据采集软件,并在G460L数控车床上进行数据的采集,利用采集的数据训练故障诊断模型。把所提故障诊断方法与相关方法进行对比,准确性达到98.75%,所提方法具有一定的有效性和实用性。

    Abstract:

    The traditional fault diagnosis methods are easy to be affected by the installation position of vibration sensor and the accuracy of fault diagnosis is not high.To overcome the problem,an attention mechanism convolutional neural networks gated recurrent unit (AM-CNN-GRU) based on attention mechanism was proposed.The current,voltage and temperature of the feed axis of NC machine tool were used as the input data in the method.To extract the spatiotemporal information in the data and improve the accuracy of fault diagnosis,a spatiotemporal feature extraction structure composed of CNN and GRU in parallel was designed.In order to verify the accuracy of the proposed method,the real-time monitoring and data acquisition software for NC machine tool was compiled by using the FOCAS data development package provided by FANUC NC,the data were collected on G460L NC lathe,and the collected data were used to train the fault diagnosis model.Comparing the proposed fault diagnosis method with the relevant methods,the accuracy is 9875%,which shows that the proposed method is effective and practical.

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许春,徐维,胡杰.基于注意力机制的数控机床进给轴深度学习故障诊断[J].机床与液压,2023,51(7):214-219.
XU Chun, XU Wei, HU Jie. Depth Learning Fault Diagnosis of NC Machine Tool Feed Axis Based on Attention Mechanism[J]. Machine Tool & Hydraulics,2023,51(7):214-219

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  • 在线发布日期: 2023-04-27
  • 出版日期: 2023-04-15