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基于VMD-MPE的开关磁阻电机功率变换器故障诊断

张竞文 熊立新 马宏昌 边敦新

张竞文, 熊立新, 马宏昌, 等 . 基于VMD-MPE的开关磁阻电机功率变换器故障诊断[J]. 北京航空航天大学学报, 2022, 48(6): 1022-1029. doi: 10.13700/j.bh.1001-5965.2020.0696
引用本文: 张竞文, 熊立新, 马宏昌, 等 . 基于VMD-MPE的开关磁阻电机功率变换器故障诊断[J]. 北京航空航天大学学报, 2022, 48(6): 1022-1029. doi: 10.13700/j.bh.1001-5965.2020.0696
ZHANG Jingwen, XIONG Lixin, MA Hongchang, et al. Fault diagnosis of switched reluctance motor power converter based on VMD-MPE[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(6): 1022-1029. doi: 10.13700/j.bh.1001-5965.2020.0696(in Chinese)
Citation: ZHANG Jingwen, XIONG Lixin, MA Hongchang, et al. Fault diagnosis of switched reluctance motor power converter based on VMD-MPE[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(6): 1022-1029. doi: 10.13700/j.bh.1001-5965.2020.0696(in Chinese)

基于VMD-MPE的开关磁阻电机功率变换器故障诊断

doi: 10.13700/j.bh.1001-5965.2020.0696
基金项目: 

国家重点研发计划 2017YFB0902800

详细信息
    通讯作者:

    熊立新, E-mail: xionglx@sdut.edu.cn

  • 中图分类号: V242.44;TM352

Fault diagnosis of switched reluctance motor power converter based on VMD-MPE

Funds: 

National Key R & D Program of China 2017YFB0902800

More Information
  • 摘要:

    故障诊断是提升开关磁阻电机(SRM)调速系统可靠性的重要技术。针对功率变换器故障信号非线性不稳定、有效信息易被噪声掩盖的特点, 提出了一种新的故障特征提取方法。对直流母线电流进行变分模态分解, 得到若干本征模态分量, 取多尺度有效模态分量排列熵平均值作为特征向量, 输入支持向量机分类器进行故障识别。为验证所提方法的可行性, 建立仿真模型, 与传统的小波分析等故障诊断方法进行对比;搭建了开关磁阻电机实验台架, 测试了开路、短路故障状态。仿真和实验结果表明:所提方法可减小噪声影响, 提高故障识别准确率。

     

  • 图 1  不对称半桥功率变换器拓扑结构

    Figure 1.  Asymmetric half-bridge power converter topology

    图 2  正常导通A相电流通路

    Figure 2.  Current path of phase A under normal conduction

    图 3  功率变换器单相故障

    Figure 3.  Single phase fault of power converter

    图 4  变分模态分解结果

    Figure 4.  Variational mode decomposition results

    图 5  采样信号在不同延迟时间下的排列熵

    Figure 5.  PE of sampled signals under different time delays

    图 6  尺度因子的影响

    Figure 6.  Influence of scale factor

    图 7  SVM分类结果

    Figure 7.  SVM classification results

    图 8  EMD-MPE和SWT-MPE分类结果

    Figure 8.  EMD-MPE and SWT-MPE classification results

    图 9  故障诊断时间对比

    Figure 9.  Comparison of fault diagnosis time

    图 10  实验系统框图

    Figure 10.  Experimental system block diagram

    图 11  实验系统

    Figure 11.  Experiment system

    图 12  实测电流

    Figure 12.  Measured current

    图 13  直流母线电流有效分量平均排列熵

    Figure 13.  Average permutation entropy of effective component of DC bus current

    表  1  不同K值对应的中心频率

    Table  1.   Center frequency of different K values

    K 中心频率/kHz
    5 1.33, 0.54, 0.26, 0.08, 0.002
    6 1.61, 0.90, 0.47, 0.25, 0.08, 0.002
    7 1.99, 1.34, 0.81, 0.45, 0.25, 0.08, 0.002
    8 2.13, 1.54, 1.04, 0.67, 0.43, 0.24, 0.08, 0.002
    9 2.36, 1.66, 1.54, 0.96, 0.63, 0.42, 0.24, 0.08, 0.002
    下载: 导出CSV

    表  2  模态分量对应的相关系数

    Table  2.   Correlation coefficients of modal components

    IMF γi
    IMF1 0.088 6
    IMF2 0.102 7
    IMF3 0.126 0
    IMF4 0.159 3
    IMF5 0.208 5
    IMF6 0.310 5
    IMF7 0.813 3
    IMF8 0.460 6
    下载: 导出CSV

    表  3  不同诊断方法的识别准确率

    Table  3.   Recognition accuracy rate of different diagnostic methods

    诊断方法 识别准确率/%
    EMD-MPE 86.67
    SWT-MPE 93.33
    VMD-MPE 100
    下载: 导出CSV

    表  4  噪声环境下分类结果

    Table  4.   Classification result under noisy environment

    诊断方法 识别准确率/%
    EMD-MPE 76.67
    SWT-MPE 90
    VMD-MPE 96.67
    下载: 导出CSV

    表  5  不同诊断方法分类结果

    Table  5.   Classification results of different diagnostic methods

    诊断方法 故障类型 测试样本数 识别结果 综合识别准确率/%
    正常 开路 短路
    EMD-MPE 正常 20 18 1 1 80
    开路 20 2 14 4
    短路 20 1 3 16
    SWT-MPE 正常 20 19 1 1 91.67
    开路 20 1 19
    短路 20 3 17
    VMD-MPE 正常 20 20 100
    开路 20 20
    短路 20 20
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-12-16
  • 录用日期:  2021-03-13
  • 刊出日期:  2022-06-20

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