小波神经网络的微孔钻削在线监测
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:


Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    提出小波神经网络对微孔钻削进行实时监测的方法,利用扭矩信号的小波包分解,以分解后的各能量向量作为神经网络的输入,对系统进行训练,利用Matlab和 LabView软件建立微孔钻削在线监测软件系统。试验结果表明:小波神经网络精度高、收敛速度快,采用小波神经网络对提高微孔钻削在线监测的准确性是有效的。

    Abstract:

    A micro-role drilling monitoring method was brought forward based on BP neural network, whose input signals were the energy eigenvectors of torque signal by using wavelet packet transform. Furthermore, a kind of micro-role drilling on-line monitoring software system has been constructed by using Matlab software and LabView software. Experiments validated that the rate of checking out micro-drills breakage was very high by using wavelet neural networks, with the characters of high-precision, quick-convergence compared with BP neural network, and the monitoring system has good practical value.

    参考文献
    相似文献
    引证文献
引用本文

孙艳红,杨兆军,崔亚新,张立新,张继生.小波神经网络的微孔钻削在线监测[J].农业机械学报,2007,38(2):176-178.[J]. Transactions of the Chinese Society for Agricultural Machinery,2007,38(2):176-178

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期:
  • 出版日期: