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卡尔曼滤波修正LS-SVM的刀具磨损识别技术研究

李威霖 傅攀 曹伟青

李威霖, 傅攀, 曹伟青. 卡尔曼滤波修正LS-SVM的刀具磨损识别技术研究[J]. 机械科学与技术, 2015, 34(1): 81-85. doi: 10.13433/j.cnki.1003-8728.2015.0117
引用本文: 李威霖, 傅攀, 曹伟青. 卡尔曼滤波修正LS-SVM的刀具磨损识别技术研究[J]. 机械科学与技术, 2015, 34(1): 81-85. doi: 10.13433/j.cnki.1003-8728.2015.0117
Li Weilin, Fu Pan, Cao Weiqing. Study on the Technology of Tool Wear Monitoring by Modifying Least Square Support Vector Machine via Kalman Filter[J]. Mechanical Science and Technology for Aerospace Engineering, 2015, 34(1): 81-85. doi: 10.13433/j.cnki.1003-8728.2015.0117
Citation: Li Weilin, Fu Pan, Cao Weiqing. Study on the Technology of Tool Wear Monitoring by Modifying Least Square Support Vector Machine via Kalman Filter[J]. Mechanical Science and Technology for Aerospace Engineering, 2015, 34(1): 81-85. doi: 10.13433/j.cnki.1003-8728.2015.0117

卡尔曼滤波修正LS-SVM的刀具磨损识别技术研究

doi: 10.13433/j.cnki.1003-8728.2015.0117
基金项目: 

国家科技重大专项项目(2010ZX04015-011)和中央高校基本科研业务费专项资金项目(SWJTU12CX039)资助

详细信息
    作者简介:

    李威霖(1986-),博士研究生,研究方向为设备智能化状态监测与故障诊断,weilin@my.swjtu.edu.cn

    通讯作者:

    傅攀,教授,博士生导师,pfu@home.swjtu.edu.cn

Study on the Technology of Tool Wear Monitoring by Modifying Least Square Support Vector Machine via Kalman Filter

  • 摘要: 针对刀具磨损状态先验样本少和常规神经网络识别模型收敛速度慢、易陷入局部极小值等问题,提出了基于最小二乘支持向量机(LS-SVM)的刀具磨损量识别技术,并针对模型输出存在系统误差而降低刀具磨损量识别精度的问题,引入卡尔曼滤波算法对时序监测结果进行修正,实现小样本下的刀具磨损量的精确识别。以车削加工为研究对象,采集加工过程中的切削力信号,应用小波包分析技术提取反映刀具磨损状态的特征信息,作为LS-SVM的输入样本,并对模型进行学习训练,完成对刀具磨损状态的识别,最后采用卡尔曼滤波修正其时序监测结果。实验结果表明:LS-SVM模型能高效地实现刀具磨损量识别,需样本数较少,训练速度快,通过卡尔曼滤波修正后的磨损量识别结果精度更高。
  • [1] Vallejo Jr A G, Nolazco-Flores J A, Rubén Morales- Menéndez, et al. Tool-wear monitoring based on continuous hidden markov models[J]. Lecture Notes in Computer Science,2005,3773:880-890
    [2] Li W L, Fu P, Cao W Q. Tool wear states recognition based on frequency-band energy analysis and fuzzy clustering[J]. Proceeding of 2010 Third International Workshop on Advanced Computational Intelligence,2010,12(57):162-167
    [3] Teti R, Jemielniak K, Donnell G O', et al. Advanced monitoring of machining operations[J]. CIRP Annals-Manufacturing Technology,2010,59(2):717-739
    [4] Roth J T, Djurdjanovic D, Yang X, et al. Quality and inspection of machining operations: tool condition monitoring[J]. Journal of Manufacturing Science and Engineering,2010,132(4):1-16
    [5] Abellan-Nebots V J, Fernando R S. A review of machining monitoring systems based on artificial intelligence process models[J]. The International Journal of Advanced Manufacturing Technology,2010,47(1-4):237-257
    [6] Dimla E, Snr D. Sensor signals for tool-wear monitoring in metal cutting operations-a review of methods[J]. International Journal of Machine Tools and Manufacture,2000,40(8):1073-1098
    [7] 王国锋,李启铭, 秦旭达,等.支持向量机在刀具磨损多状态监测中的应用[J].天津大学学报,2011,44(1):35-39 Wang G F, Li Q M, Qin X D, et al. Application of support-vector-machine in tool wear of multi-stage monitoring[J]. Journal of Tianjin University,2011,44(1):35-39 (in Chinese)
    [8] Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural Processing Letters,1999,9(3):293-300
    [9] Kalman R E. A new approach to linear filtering and prediction problems[J]. Journal of Basic Engineering,1960,82(1):35-45
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出版历程
  • 收稿日期:  2013-06-25
  • 刊出日期:  2015-01-05

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