东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2003, Vol. 24 ›› Issue (3): 248-251.DOI: -

• 论著 • 上一篇    下一篇

基于多传感器融合的磨削砂轮钝化的智能监测

巩亚东;吕洋;王宛山;朱晓峰   

  1. 东北大学机械工程与自动化学院;东北大学机械工程与自动化学院;东北大学机械工程与自动化学院;丹东贝特自动化工程仪表有限公司 辽宁沈阳110004
  • 收稿日期:2013-06-23 修回日期:2013-06-23 出版日期:2003-03-15 发布日期:2013-06-23
  • 通讯作者: Gong, Y.-D.
  • 作者简介:-
  • 基金资助:
    教育部科学技术研究重点资助项目 ( 2 0 0 3 2 )

Intelligent monitoring for grinding wheel passivation based on multisensor fusion

Gong, Ya-Dong (1); Lu, Yang (1); Wang, Wan-Shan (1); Zhu, Xiao-Feng (2)   

  1. (1) Sch. of Mech. Eng. and Automat., Northeastern Univ., Shenyang 110004, China; (2) Best Automat. Eng. and Meter Co. Ltd, Dandong 118000, China
  • Received:2013-06-23 Revised:2013-06-23 Online:2003-03-15 Published:2013-06-23
  • Contact: Gong, Y.-D.
  • About author:-
  • Supported by:
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摘要： 利用多传感器信息融合技术 ,通过模糊分类的方法对不同的磨削条件进行模糊化处理 ,构建了砂轮钝化监测多传感器融合系统结构 ;应用BP神经网络将磨削过程中声发射、磨削力和功率传感器信号合理融合 ,提出了自适应变学习率策略 ,将其神经网络输出的信号特征值作为表征砂轮钝化状态识别的判据 ,进行了砂轮钝化监测实验·结果表明 ,使用多传感器信息融合方法比使用单一传感器方法识别率高 ,监测效果好 ,并可实现智能监控和及时修整砂轮

关键词: 多传感器融合, 砂轮钝化, 模糊分类, BP神经网络, 智能监测, 磨削

Abstract: The multi-sensor fusion system was established to monitor the passivation status of grinding wheel. BP neural network was used to interfuse reasonably the acoustic emission signal, grinding force signal and power sensor signal. The output eigenvalue of the neural network was considered as a criterion to distinguish the passivation status of grinding wheel. A selfadaptive learning strategy was brought forward. A grinding-wheel passivation experiment was done. The multi-sensor fusion method can get higher recognition rate and better monitoring effect than a single sensor. In this way, an intelligent monitor can be realized and the grinding wheel can be dressed in time.

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