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深度在线小波极限学习在旋转机械故障诊断中的应用

王椿晶 王海瑞

王椿晶, 王海瑞. 深度在线小波极限学习在旋转机械故障诊断中的应用[J]. 机械科学与技术, 2023, 42(7): 1029-1034. doi: 10.13433/j.cnki.1003-8728.20220046
引用本文: 王椿晶, 王海瑞. 深度在线小波极限学习在旋转机械故障诊断中的应用[J]. 机械科学与技术, 2023, 42(7): 1029-1034. doi: 10.13433/j.cnki.1003-8728.20220046
WANG Chunjing, WANG Hairui. Application of Depth Online Wavelet Extreme Learning Machine in Rotating Machinery Fault Diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(7): 1029-1034. doi: 10.13433/j.cnki.1003-8728.20220046
Citation: WANG Chunjing, WANG Hairui. Application of Depth Online Wavelet Extreme Learning Machine in Rotating Machinery Fault Diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(7): 1029-1034. doi: 10.13433/j.cnki.1003-8728.20220046

深度在线小波极限学习在旋转机械故障诊断中的应用

doi: 10.13433/j.cnki.1003-8728.20220046
基金项目: 

国家自然科学基金项目 61863016

详细信息
    作者简介:

    王椿晶(1996-), 硕士研究生, 研究方向为人工智能, 故障诊断, 934039538@qq.com

    通讯作者:

    王海瑞, 教授, 博士, wang102996@163.com

  • 中图分类号: TH17

Application of Depth Online Wavelet Extreme Learning Machine in Rotating Machinery Fault Diagnosis

  • 摘要: 由于旋转机械故障诊断模型训练时间长,容易过拟合以及传统的极限学习机只能处理批量数据,实效性差等问题。提出一种基于深度在线小波极限学习机的旋转机械故障诊断方法。将自编码器的思想引入小波极限学习机中,堆叠形成WELM-AE,将底层的故障特征向更加抽象的高级特征转换。再采用在线极限学习机作为顶层分类器进行故障识别。实验结果验证:该算法在旋转机械故障诊断上的可行性,继承了极限学习机训练速度快的特点,相较于BP、SVM、SAE、CNN有更高的准确率。
  • 图  1  WELM-AE结构图

    Figure  1.  WELM-AE structure diagram

    图  2  DWOSELM结构图

    Figure  2.  DWOSELM structure diagram

    图  3  不同激活函数10次实验平均准确率

    Figure  3.  3 Average accuracy of 10 experiments with different activation functions

    图  4  不同小波函数10次实验平均诊断准确率

    Figure  4.  Average diagnostic accuracy of 10 experiments with different wavelet functions

    图  5  优化算法适应度曲线

    Figure  5.  Fitness curve of the optimization algorithm

    图  6  各模型分类评价结果

    Figure  6.  Evaluation results for each model classification

    图  7  诊断结果混淆矩阵

    Figure  7.  The confusion matrix of the diagnostic results

    图  8  各模型分类评价结果

    Figure  8.  Evaluation results for each model classification

    图  9  诊断结果混淆矩阵

    Figure  9.  The confusion matrix of the diagnostic results

    表  1  隐含层参数选择

    Table  1.   Selection of hidden layer parameters

    隐含层参数选择 准确率/% 时间/s
    N1=10, N2=10 89.17 3.15
    N1=10, N2=20 89.72 3.62
    N1=10, N2=30 89.72 4.07
    N1=20, N2=20 93.89 4.20
    N1=20, N2=30 93.61 4.94
    N1=30, N2=30 95.00 5.80
    N1=30, N2=40 94.44 6.42
    N1=10, N2=10, N3=10 90.28 3.53
    N1=10, N2=20, N3=30 90.56 5.45
    N1=20, N2=20, N3=20 93.89 4.98
    N1=30, N2=30, N3=30 95.28 6.19
    N1=40, N2=40, N3=40 94.44 8.92
    下载: 导出CSV

    表  2  齿轮箱故障类别

    Table  2.   Gearbox fault categories

    运行状态 类别
    正常 1
    点蚀故障 2
    点磨(大齿轮点蚀和小齿轮磨损) 3
    断齿故障 4
    断磨(大齿轮断齿和小齿轮磨损) 5
    磨损故障 6
    下载: 导出CSV

    表  3  诊断结果对比

    Table  3.   Comparison of diagnostic results

    模型 平均准确率/% 训练时间/s
    SVM 90.73 0.30
    BP 91.24 0.20
    ELM 92.67 0.19
    SAE 91.85 9.19
    CNN 93.29 17.19
    DELM 92.59 2.95
    本文方法 95.35 5.32
    下载: 导出CSV

    表  4  滚动轴承故障类别

    Table  4.   Rolling bearing fault categories

    运行状态 类别标签
    正常 1
    滚动体故障 2
    内圈故障 3
    外圈故障 4
    下载: 导出CSV

    表  5  诊断结果对比

    Table  5.   Comparison of diagnostic results

    模型 平均准确率/% 训练时间/s
    SVM 92.56 0.21
    BP 90.73 0.19
    ELM 93.67 0.16
    SAE 91.85 6.50
    CNN 92.78 14.23
    DELM 90.36 1.82
    本文方法 96.73 4.74
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-22
  • 刊出日期:  2023-07-25

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