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改进VMD-LSTM法在刀具磨损状态识别中的应用

姜超 李国富

姜超, 李国富. 改进VMD-LSTM法在刀具磨损状态识别中的应用[J]. 机械科学与技术, 2022, 41(2): 246-252. doi: 10.13433/j.cnki.1003-8728.20200627
引用本文: 姜超, 李国富. 改进VMD-LSTM法在刀具磨损状态识别中的应用[J]. 机械科学与技术, 2022, 41(2): 246-252. doi: 10.13433/j.cnki.1003-8728.20200627
JIANG Chao, LI Guofu. Application of Modified VMD and LSTM in Tool Wear State Recognition Model[J]. Mechanical Science and Technology for Aerospace Engineering, 2022, 41(2): 246-252. doi: 10.13433/j.cnki.1003-8728.20200627
Citation: JIANG Chao, LI Guofu. Application of Modified VMD and LSTM in Tool Wear State Recognition Model[J]. Mechanical Science and Technology for Aerospace Engineering, 2022, 41(2): 246-252. doi: 10.13433/j.cnki.1003-8728.20200627

改进VMD-LSTM法在刀具磨损状态识别中的应用

doi: 10.13433/j.cnki.1003-8728.20200627
基金项目: 

国家自然科学基金项目 51705263

详细信息
    作者简介:

    姜超(1997-), 硕士研究生, 研究方向为信号处理与故障诊断, 947030121@qq.com

    通讯作者:

    李国富, 教授, 硕士生导师, liguofu@nbu.edu.cn

  • 中图分类号: TH164

Application of Modified VMD and LSTM in Tool Wear State Recognition Model

  • 摘要: 针对车刀在实际加工时工况复杂导致磨损状态识别精度不高的问题,提出了一种基于最大包络峰度法的变分模态分解(Variational mode decomposition,VMD)结合长短时记忆网络(Long short-term memory,LSTM)的组合分类算法。采用最大包络峰度法确定VMD最佳分解模态数,计算信噪比对高频信号进行降噪重构,然后对原始信号以及分解后的信号进行特征提取和清洗,针对数据样本不均衡的问题,引入SMOTE算法合成少数类样本,结合特征变化以及刀具加工过程中的磨损划分数据集,使用LSTM模型实现多工况下车刀磨损状态的分类。最后通过实验验证所提出的模型和方法的有效性,实验结果表明,此模型与其他分类模型相比具有更高的分类精度以及更好的泛化性。
  • 图  1  试验流程图

    图  2  车刀信号与频谱图

    图  3  最大包络峰度变化趋势图

    图  4  VMD分解波形图

    图  5  去噪后信号

    图  6  特征变化趋势图

    图  7  模型精度训练和损失曲线

    表  1  试验切削条件表

    组号 转速/ (r·min-1) 切削深度/mm 进给速度/ (mm·r-1)
    1 480 1.0 0.176
    2 760 1.0 0.176
    2 760 1.5 0.192
    3 1 080 1.0 0.176
    下载: 导出CSV

    表  2  实验样本数量表

    组号 1 2 3 4
    样本数量 273 244 190 214
    下载: 导出CSV

    表  3  LSTM模型结构

    LSTM模型结构 各层神经元数量 各层激励函数 各层输入维度
    输入层 特征维度 (None, 1, 特征维度)
    LSTM层 30 Sigmoid (None, 30)
    全连接层 8 Relu (None, 8)
    输出层 3 Softmax (None, 3)
    下载: 导出CSV

    表  4  模型测试结果

    分类模型 测试集平均分类准确率/% 测试集最优分类准确率/% 训练时间/s
    LSTM 93.78 94.02 83.35
    PSO-SVM 90.43 91.67 89.49
    BP神经网络 88.59 89.21 39.86
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-05-21
  • 刊出日期:  2022-02-25

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