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改进蝙蝠算法优化支持向量机的故障诊断方法

张凡 孙文磊 王宏伟 徐甜甜

张凡,孙文磊,王宏伟, 等. 改进蝙蝠算法优化支持向量机的故障诊断方法[J]. 机械科学与技术,2023,42(3):446-452 doi: 10.13433/j.cnki.1003-8728.20200583
引用本文: 张凡,孙文磊,王宏伟, 等. 改进蝙蝠算法优化支持向量机的故障诊断方法[J]. 机械科学与技术,2023,42(3):446-452 doi: 10.13433/j.cnki.1003-8728.20200583
ZHANG Fan, SUN Wenlei, WANG Hongwei, XU Tiantian. A Fault Diagnosis Method Based on Improved Bat Algorithm Optimization Support Vector Machine[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(3): 446-452. doi: 10.13433/j.cnki.1003-8728.20200583
Citation: ZHANG Fan, SUN Wenlei, WANG Hongwei, XU Tiantian. A Fault Diagnosis Method Based on Improved Bat Algorithm Optimization Support Vector Machine[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(3): 446-452. doi: 10.13433/j.cnki.1003-8728.20200583

改进蝙蝠算法优化支持向量机的故障诊断方法

doi: 10.13433/j.cnki.1003-8728.20200583
基金项目: 国家自然科学基金项目(51565055)
详细信息
    作者简介:

    张凡(1997−),硕士研究生,研究方向为风力发电机主要部件的故障诊断,1714711901@qq.com

    通讯作者:

    孙文磊,教授,博士,sunwenxj@163.com

  • 中图分类号: TH133.3

A Fault Diagnosis Method Based on Improved Bat Algorithm Optimization Support Vector Machine

  • 摘要: 提出了一种基于变分模态分解(VMD)和时移多尺度散布熵(TSMDE)的故障特征提取结合改进的蝙蝠算法(IBA)来优化支持向量机(SVM)的滚动轴承故障诊断方法。通过变分模态分解,避免了模式混叠问题,提取各模态分量的散布熵构造故障特征向量,作为故障诊断模型的输入;提出了一种新的自适应速度权重因子用于构建改进的蝙蝠算法以优化支持向量机(IBA-SVM),实现了对不同故障类型的轴承进行分类;利用实验数据对提出的诊断方法进行验证,并与用粒子群算法(PSO)优化支持向量机(PSO-SVM)的诊断方法进行对比。结果表明所提出的方法分类准确率更高,用时更少。
  • 图  1  故障诊断模型流程图

    图  2  原始振动信号

    图  3  K = 3 内圈故障光谱图

    图  4  最佳模态分量时域波形图

    图  5  熵方法特征信号提取结果

    图  6  参数m对于特征提取的影响

    图  7  参数c对于特征提取的影响

    图  8  测试集分类结果

    表  1  数据采集装置参数

    故障
    类型
    轴承型号电机转速/
    (r·min−1
    故障尺寸/
    mm
    采样
    频率/
    kHz
    采样
    时间/
    s
    内圈
    故障
    6205-2RS JEM SKF1772 0.177 81200010
    下载: 导出CSV

    表  2  不同分解层数下的中心频率

    分解层数K中心频率/Hz
    3 691 2757 3572
    4 616 1349 2761 3572
    5 615 1347 2750 3623 3623
    下载: 导出CSV

    表  3  各模态分量熵值

    IMF1IMF2IMF3
    NR 2.2093 3.1791 3.0544
    IR 3.2479 3.4435 3.4545
    OR 3.5113 3.5294 3.1390
    BR 2.7063 3.5787 3.5670
    下载: 导出CSV

    表  4  TSMDE和MDE分类结果

    准确率/%时间/s
    TSMDE10022.62
    MDE93.669.84
    下载: 导出CSV

    表  5  IBA算法参数设置

    种群
    数量
    变量
    维度
    最大迭代
    次数
    参数c搜索
    范围
    参数σ搜索
    范围
    502100[1,100][1,100]
    下载: 导出CSV

    表  6  3种模型分类结果

    模型准确率/%时间/s
    IBA-SVM10016.57
    BA-SVM10020.05
    PSO-SVM100120.85
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-03-02
  • 刊出日期:  2023-03-25

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