东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2017, Vol. 38 ›› Issue (10): 1426-1430.DOI: 10.12068/j.issn.1005-3026.2017.10.012

• 机械工程 • 上一篇    下一篇

基于小波奇异性和支持向量机微铣刀破损检测

刘宇1， 王迁1， 刘阔2， 张义民1   

  1. (1. 东北大学 机械工程与自动化学院， 辽宁 沈阳110819; 2. 大连理工大学 精密与特种加工教育部重点实验室， 辽宁 大连116024)
  • 收稿日期:2016-04-25 修回日期:2016-04-25 出版日期:2017-10-15 发布日期:2017-10-13
  • 通讯作者: 刘宇
  • 作者简介:刘宇(1980-)，男，宁夏中卫人，东北大学副教授； 张义民(1958-)，男，吉林长春人，东北大学教授，博士生导师，教育部“长江学者奖励计划”特聘教授.
  • 基金资助:
    国家自然科学基金资助项目(51105067，51135003)； 中央高校基本科研业务费专项资金资助项目(N120403011).

Micro Milling Cutter Breakage Detection Based on Wavelet Singularity and Support Vector Machine

LIU Yu1， WANG Qian1， LIU Kuo2， ZHANG Yi-min1   

  1. 1. School of Mechanical Engineering & Automation， Northeastern University， Shenyang 110819， China; 2. Key Laboratory for Precision and Non-traditional Machining Technology， Ministry of Education， Dalian University of Technology， Dalian 116024， China.
  • Received:2016-04-25 Revised:2016-04-25 Online:2017-10-15 Published:2017-10-13
  • Contact: LIU Yu
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摘要： 针对微铣削过程中刀刃破损的现象，提出了基于振动信号奇异性分析的自学习式支持向量机的刀具破损检测方法.对两种状态信号作连续小波变换，计算小波模极大值和信号的李普希兹指数(Lips).通过Lips识别刀具状态，拟合Lips分布概率密度函数并验证其符合正态分布，将Lips分布的均值、方差作为特征值，通过遗传算法参数寻优建立了刀具破损状态的支持向量机(SVM)识别模型，也称最优模型.利用最优模型预测刀具破损状态，其预测准确度从84%逐步提高至90%，提升了系统预测模型的鲁棒性.最后通过实验验证了该方法的有效性.

关键词: 刀具破损, 微铣削, 小波奇异性, 支持向量机, 自学习

Abstract: A tool breakage detection method was proposed based on the singularity analysis of vibration signal and self-learning support vector machine. The measured vibration signals were decomposed by the continuous wavelet transform， the wavelet modulus maxima (WTMM) and the Lipschitz index (Lips) were calculated. The state of tool breakage was recognized by Lips， and the Lips probability density function was fitted， which obeys the normal distribution. The support vector machine identification model of tool state was established by the parameter optimization of genetic algorithm based on mean value and variance of Lips (also called the optimal model). The tool breakage state was predicted by using this model， of which prediction accuracy increased gradually from 84% to 90%， and the robust of system prediction model was improved. Finally， the effectiveness of this method was verified by the experiments.

Key words: tool breakage, micro milling, wavelet singularity, support vector machine, self-learning

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