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基于多特征融合的电磁换向阀故障模式识别

马栋 刘志浩 高钦和 黄通

马栋,刘志浩,高钦和,等. 基于多特征融合的电磁换向阀故障模式识别[J]. 北京航空航天大学学报,2023,49(4):913-921 doi: 10.13700/j.bh.1001-5965.2021.0367
引用本文: 马栋,刘志浩,高钦和,等. 基于多特征融合的电磁换向阀故障模式识别[J]. 北京航空航天大学学报,2023,49(4):913-921 doi: 10.13700/j.bh.1001-5965.2021.0367
MA D,LIU Z H,GAO Q H,et al. Solenoid directional control valve fault pattern recognition based on multi-feature fusion[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(4):913-921 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0367
Citation: MA D,LIU Z H,GAO Q H,et al. Solenoid directional control valve fault pattern recognition based on multi-feature fusion[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(4):913-921 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0367

基于多特征融合的电磁换向阀故障模式识别

doi: 10.13700/j.bh.1001-5965.2021.0367
基金项目: 国家自然科学基金(51905541);陕西省自然科学基础研究计划(2020JQ487);陕西省高校科协青年人才托举计划(20190412)
详细信息
    通讯作者:

    E-mail:liuzh_epgc@163.com

  • 中图分类号: TH137.52+3

Solenoid directional control valve fault pattern recognition based on multi-feature fusion

Funds: National Natural Science Foundation of China (51905541); Natural Science Basic Research Program of Shaanxi (2020JQ487); Young Talent Fund of University Association for Science and Technology in Shaanxi, China (20190412)
More Information
  • 摘要:

    为提高基于驱动端电流检测的电磁换向阀故障诊断方法的可靠性和识别准确度,开展了电磁换向阀故障模式识别方法研究。提出一种基于多特征融合的方法对电流信号时频分析和时域参数的特征值提取融合;通过设计电磁换向阀驱动端电流信号的采集实验,获取电磁换向阀驱动端电流的时域信号和二阶变化率的多特征曲线,提取时域参数及二阶变化率相应频带能量作为特征值,构建多特征融合的特征向量;采用基于径向基核函数的多分类支持向量机对电磁换向阀进行模式识别。结果表明:基于多特征融合的支持向量机较基于能量特征值的支持向量机可提升8.7%的识别精度和42.11%的验证准确率。

     

  • 图 1  小波包提取特征值步骤

    Figure 1.  Wavelet packet extraction extraction step

    图 2  三层小波包分解示意图

    注:S为原始信号;A为高频;D为低频;数字为分解的层数。

    Figure 2.  Schematic diagram of three-layer wavelet packet decomposition

    图 3  电磁换向阀故障诊断流程

    Figure 3.  Solenoid directional control valve fault diagnosis process

    图 4  4WE10E31B/CG24N9Z5L型电磁换向阀

    Figure 4.  4WE10E31B/CG24N9Z5L solenoid directional control valve

    图 5  驱动端电流检测系统

    Figure 5.  Drive end current detection system

    图 6  驱动端电流和阀芯位移

    Figure 6.  Drive end current and spool displacement

    图 7  不同状态下电磁换向阀驱动端电流

    Figure 7.  Solenoid directional control valve drive end current in different states

    图 8  不同状态下电流信号二阶变化率

    Figure 8.  Second-order guide of current signal in different states

    图 9  小波重构信号

    Figure 9.  Wavelet reconstruction signal

    图 10  不同特征向量训练集预测结果

    Figure 10.  Prediction results of different feature vector training sets

    图 11  不同特征向量测试集预测结果

    Figure 11.  Prediction results of different feature vector test sets

    表  1  不同SVM准确率对比

    Table  1.   Comporison of different SVM accuracy rates

    SVM类型训练准确率/%测试准确率/%五折交叉验证准确率/%
    C-SVC10091.3050
    υ-SVC94.7484.7850
    ε-SVR10086.96
    υ-SVR10086.96
    下载: 导出CSV

    表  2  不同核函数SVM准确率对比

    Table  2.   Comporison of SVM accuracy rates of different kernel functions

    核函数训练准确率/%测试准确率/%五折交叉验证准确率/%
    Polynomial97.7489.1350
    RBF10091.3050
    Sigmoid76.3267.3942.11
    下载: 导出CSV

    表  3  四种不同状况特征向量

    Table  3.   Four feature vectors under different situations

    电磁换向阀状态组号E30E31E32E33E34E35E36E37
    阀芯正常第1组0.08710.10960.08290.19200.29440.62470.15290.6606
    第2组0.07690.10600.09970.21090.24330.65770.19450.6315
    弹簧断裂第1组0.01210.03020.09690.08450.60500.53770.21560.5299
    第2组0.00130.00340.14210.17590.62160.70140.17770.1972
    阀芯卡死第1组0.02190.07470.14920.15350.63680.43060.30470.5142
    第2组0.02260.03880.08920.05740.58490.47900.24790.5947
    阀芯轻微卡滞第1组0.03260.16080.17680.11040.24580.48690.21960.7642
    第2组0.04000.08930.22420.17920.32240.74580.22000.4467
    下载: 导出CSV

    表  4  各主元贡献率

    Table  4.   Contribution rate of each principal

    主元数贡献率/%
    160.0411
    229.1488
    39.0540
    41.6772
    50.0659
    60.0071
    70.0051
    80.0005
    90.0002
    10 0.0001
    11 1.21×10−5
    下载: 导出CSV

    表  5  前4主元系数

    Table  5.   First 4 pivot coefficients

    时域信号第1主元第2主元第3主元第4主元
    p1−0.33330.26890.1710.1703
    p2−0.2795−0.330−0.2900.5124
    p3−0.25940.4140.03560.1565
    p40.28710.22450.5244−0.3011
    p5−0.33330.26870.17190.1717
    p60.24690.3678−0.4031−0.0773
    p70.35370.09890.30410.520
    p80.35290.2034−0.02670.4869
    p9−0.3500.12160.37780.0651
    p100.3101−0.30420.24490.2126
    p110.14390.4806−0.34990.0305
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-07-01
  • 录用日期:  2021-11-01
  • 网络出版日期:  2021-11-26
  • 整期出版日期:  2023-04-30

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