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平衡分布自适应的变工况轴承故障迁移诊断研究

王廷轩 刘韬 刘应东 王振亚

王廷轩,刘韬,刘应东, 等. 平衡分布自适应的变工况轴承故障迁移诊断研究[J]. 机械科学与技术,2023,42(8):1316-1323 doi: 10.13433/j.cnki.1003-8728.20220058
引用本文: 王廷轩,刘韬,刘应东, 等. 平衡分布自适应的变工况轴承故障迁移诊断研究[J]. 机械科学与技术,2023,42(8):1316-1323 doi: 10.13433/j.cnki.1003-8728.20220058
WANG Tingxuan, LIU Tao, LIU Yingdong, WANG Zhenya. Bearing Fault Transfer Diagnosis with Balanced Distribution Adaptation Under Variable Working Conditions[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(8): 1316-1323. doi: 10.13433/j.cnki.1003-8728.20220058
Citation: WANG Tingxuan, LIU Tao, LIU Yingdong, WANG Zhenya. Bearing Fault Transfer Diagnosis with Balanced Distribution Adaptation Under Variable Working Conditions[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(8): 1316-1323. doi: 10.13433/j.cnki.1003-8728.20220058

平衡分布自适应的变工况轴承故障迁移诊断研究

doi: 10.13433/j.cnki.1003-8728.20220058
基金项目: 国家自然科学基金项目(52065030)与云南省重大科技专项计划项目(202202AC080008)
详细信息
    作者简介:

    王廷轩(1994−),硕士研究生,研究方向为迁移学习、设备状态监测及故障诊断,1043423651@qq.com

    通讯作者:

    刘韬,教授,博士,kmliutao@aliyun.com

  • 中图分类号: TN98;TN06;TH165.3

Bearing Fault Transfer Diagnosis with Balanced Distribution Adaptation Under Variable Working Conditions

  • 摘要: 针对机器学习方法中样本满足独立同分布导致的诊断精度低下问题,提出了平衡分布自适应(BDA)算法与K-最近邻(KNN)分类算法结合的轴承故障迁移诊断方法。提取变工况下的轴承故障信号的时域特征分别作为源域和目标域,并利用Fisher判别分析方法优选特征;基于BDA将不同工况的特征样本映射至可再生希尔伯特空间,引入最大均值差异(MMD)对变工况样本的边缘分布差异和条件分布差异进行适配;利用KNN分类器对分布适配后的样本进行迁移诊断。仿真和实验表明:本文方法在同实验平台和跨实验平台迁移上,轴承诊断的准确性和分布距离相较于其他算法优势明显。
  • 图  1  平衡分布自适应故障迁移诊断流程图

    Figure  1.  Flowchart for balancing distribution adaptive fault transfer diagnosis

    图  2  仿真样本分布适配散点图及概率密度曲线

    Figure  2.  Scatter plot and probability density curve of simulated sample distribution adaptation

    图  3  轴承故障试验台

    Figure  3.  Bearing fault test rig

    图  4  时域特征所占权重

    Figure  4.  Weights of time-domain characteristics

    图  5  同实验平台迁移的可视化散点图

    Figure  5.  Visual scatter plot of transfer within the same experimental platform

    图  6  同实验平台迁移的MMD适配距离

    Figure  6.  MMD adaptation distance of transfer within the same experimental platform

    图  7  跨实验平台迁移的可视化散点图

    Figure  7.  Visual scatter plot of transfer across different experimental platforms

    图  8  跨实验平台迁移的MMD适配距离

    Figure  8.  MMD adaptation distance for transfer across different experimental platforms

    表  1  CWRU轴承样本集

    Table  1.   CWRU bearing sample set

    类型采样频率/kHz转速/(r·min−1故障直径/mm
    正常故障48 1797 0
    外圈故障481797 0.7112
    内圈故障4817970.7112
    滚子故障4817970.7112
    正常故障121772 0
    外圈故障121772 0.7112
    内圈故障121772 0.7112
    滚子故障1217720.7112
    正常故障1217500
    外圈故障121750 0.7112
    内圈故障1217500.7112
    滚子故障1217500.7112
    下载: 导出CSV

    表  2  实验室轴承样本集

    Table  2.   Laboratory bearing sample set

    类型采样频率/kHz转速/(r·min−1故障直径/mm
    正常故障25.6600 0
    外圈故障25.6600 1
    内圈故障25.6600 1
    滚子故障25.66001
    正常故障25.6800 0
    外圈故障25.6800 1
    内圈故障25.6800 1
    滚子故障25.68001
    正常故障25.61200 0
    外圈故障25.61200 1
    内圈故障25.61200 1
    滚子故障25.612001
    下载: 导出CSV

    表  3  时域特征函数表达式

    Table  3.   Time-domain characteristic functional expressions

    时域特征函数表达式
    有效值 $ {X_{{\rm{RMS}}}} = \sqrt {\dfrac{1}{n}\displaystyle\sum\limits_{i = 1}^n {{{\left| {x(i)} \right|}^2}} } $
    歪度 $ K = \dfrac{{ \dfrac{1} {n}\displaystyle\sum\limits_{i = 1}^n {{{\left[ {x(i) - \mu } \right]}^3}} }}{{{\sigma ^3}}} $
    峭度 $ K = \dfrac{{ \dfrac{1}{n}\displaystyle\sum\limits_{i = 1}^n {{{\left[ {x(i) - \mu } \right]}^4}} }}{{{\sigma ^4}}} $
    峰值 $ {X_{\max }} = \max \left[ {x(i)} \right] $
    峰峰值 $ X = {X_{\max }} - {X_{\min }} $
    波形因数 $ W = \dfrac{{{X_{\rm {RMS}}}}}{{\left| {\bar x} \right|}} $
    脉冲因数 $ I = \dfrac{{{X_{\max }}}}{{{X_{\rm {RMS}}}}} $
    峰值因数 $ C = \dfrac{{{X_p}}}{{{X_{\rm {RMS}}}}} $
    裕度 $ L = \dfrac{{{X_p}}}{{{X_r}}} $
    下载: 导出CSV

    表  4  同实验平台迁移的不同分布差异准确率

    Table  4.   Accuracy of different distribution differences in transfer within the same experimental platform %

    迁移任务300 r/min→
    800 r/min
    600 r/min→
    1 200 r/min
    800 r/min→
    1 200 r/min
    KNN56.6755.0858.24
    PCA57.0256.9859.64
    TCA73.8376.6274.54
    JDA86.2385.9888.96
    BDA10099.42100
    下载: 导出CSV

    表  5  跨实验平台迁移的不同分布差异准确率

    Table  5.   Accuracy of different distribution differences in transfer across different experimental platforms %

    迁移任务1 797 r/min→
    1 200 r/min
    1 772 r/min→
    1 200 r/min
    1 750 r/min→
    1 200 r/min
    KNN76.6775.0878.24
    PCA44.5447.0342.59
    TCA61.7362.8859.25
    JDA76.6286.1778.61
    BDA98.5793.4687.73
    下载: 导出CSV
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  • 收稿日期:  2021-07-05
  • 刊出日期:  2023-08-31

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