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基于CEEMD与改进的ELM旋转整流器故障诊断

朱佩荣 刘勇智 刘棕成 陈俊柏 聂恺

朱佩荣,刘勇智,刘棕成,等. 基于CEEMD与改进的ELM旋转整流器故障诊断[J]. 北京航空航天大学学报,2023,49(5):1166-1175 doi: 10.13700/j.bh.1001-5965.2021.0376
引用本文: 朱佩荣,刘勇智,刘棕成,等. 基于CEEMD与改进的ELM旋转整流器故障诊断[J]. 北京航空航天大学学报,2023,49(5):1166-1175 doi: 10.13700/j.bh.1001-5965.2021.0376
ZHU P R,LIU Y Z,LIU Z C,et al. Fault diagnosis of synchronous generator rotating rectifier based on CEEMD and improved ELM[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(5):1166-1175 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0376
Citation: ZHU P R,LIU Y Z,LIU Z C,et al. Fault diagnosis of synchronous generator rotating rectifier based on CEEMD and improved ELM[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(5):1166-1175 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0376

基于CEEMD与改进的ELM旋转整流器故障诊断

doi: 10.13700/j.bh.1001-5965.2021.0376
基金项目: 西安市青年人才托举计划项目(095920201309)
详细信息
    通讯作者:

    E-mail:liuyz_kj@163.com

  • 中图分类号: V242.44

Fault diagnosis of synchronous generator rotating rectifier based on CEEMD and improved ELM

Funds: Xi’an Youth Talent Lift Project (095920201309)
More Information
  • 摘要:

    针对目前应用于航空发电机旋转整流器故障诊断中的人工智能算法存在诊断速度慢、参数选取困难等问题,提出一种基于互补式集合经验模态分解(CEEMD)与樽海鞘优化的极限学习机(SSA-ELM)故障诊断方法。在有限元软件Maxwell与Simplorer中搭建三级式电机模型,采集励磁电流信号,利用CEEMD将励磁电流信号分解为一系列模态分量,构建故障特征参量,再通过樽海鞘群算法(SSA)优化极限学习机的训练参数$ \omega $$ b $,并对故障进行诊断,最后通过实验平台验证所提方法。结果证明了三级式同步电机有限元模型的有效性,所提方法相校于现有方法,具有更高的故障诊断准确率与分类速度。

     

  • 图 1  主发电机有限元2D模型

    Figure 1.  2D finite element model of main generator

    图 2  三级式同步电机整体模型

    Figure 2.  Integral model of three stage synchronous motor

    图 3  调压器模型

    Figure 3.  Voltage regulator model

    图 4  主发电机三相输出电压波形

    Figure 4.  Three phase output voltage waveform of main generator

    图 5  CEEMD信号分解流程

    Figure 5.  Signal decomposition flow chart of CEEMD

    图 6  SSA-ELM算法流程

    Figure 6.  Flow chart of SSA-ELM algorithm

    图 7  5类故障状态下的励磁电流信号

    Figure 7.  Excitation current signals under five fault states

    图 8  D1二极管开路时励磁电流IMF分量的时域图

    Figure 8.  Time domain diagram of IMF component of excitation current in D1 diode open circuit

    图 9  D1二极管开路时IMF分量能量熵

    Figure 9.  Energy entropy of IMF component in D1 diode open circuit

    图 10  不同故障状态下的能量熵

    Figure 10.  Energy entropy under different fault states

    图 11  本文方法的故障诊断结果

    Figure 11.  Fault diagnosis results based on proposed method

    图 12  三级式同步发电机实验台

    Figure 12.  Three stage synchronous generator experiment bed

    图 13  空载条件下D1二极管开路励磁电流

    Figure 13.  Waveform of D1 diode open circuit excitation current under no load condition

    表  1  主发电机基本参数

    Table  1.   Basic parameters of main generator

    额定功率/kVA额定电压/V额定频率/Hz极对数
    401154002
    下载: 导出CSV

    表  2  二极管开路故障下的5类故障状态

    Table  2.   Five kinds of fault states under diode open circuit fault

    故障类型状态描述故障二极管
    类别Ⅰ二极管正常
    类别Ⅱ单个二极管开路D1、D2、D3、D4、D5、D6
    类别Ⅲ同桥臂双二极管开路D1D6、D2D5、D3D4
    类别Ⅳ不同桥臂双二极管开路
    (同为上桥臂或下桥臂)
    D1D2、D1D3、D2D3、D6D5、D6D4、D5D4
    类别Ⅴ不同桥臂双二极管开路
    (一个为上桥臂一个为下桥臂)
    D1D5、D1D4、D2D6、D2D4、D3D6、D3D5
    下载: 导出CSV

    表  3  不同负载条件下各分类方法仿真结果性能对比

    Table  3.   Simulation result of performance comparison of different classification methods under different load conditions

    负载条件训练时间/s测试时间/s
    本文方法ELMPSO-ELMSVMSSA-SVM本文方法ELMPSO-ELMSVMSSA-SVM
    空载10.25980.012810000.01290.012784.0010.965314.65250.0127
    1.5 kW负载10.35640.013510000.01450.013686.0011.325614.78690.0145
    3 kW负载10.32560.013910000.01420.013787.6010.256914.65980.0144
    混合负载29.85790.040510000.04350.041281.2032.154644.26980.0412
    负载条件准确率/%诊断方差
    本文方法ELMPSO-ELMSVMSSA-SVM本文方法ELMPSO-ELMSVMSSA-SVM
    空载95.602.568982.53200.023291.600.236520.30120.03621000
    1.5 kW负载96.403.256483.23650.021094.000.258920.25620.03591000
    3 kW负载95.602.354682.45620.023493.200.354520.25640.03621000
    混合负载95.608.2546247.60200.069489.60.845660.65260.10891000
    下载: 导出CSV

    表  4  不同负载条件下各分类方法实验结果性能对比

    Table  4.   Experimental results of performance comparison of different classification methods under different load conditions

    负载条件训练时间/s测试时间/s
    本文方法ELMPSO-ELMSVMSSA-SVM本文方法ELMPSO-ELMSVMSSA-SVM
    空载10.25980.012810000.01290.012784.0010.248914.65250.0127
    1.5 kW负载10.35640.013510000.01450.013686.0010.258714.78690.0145
    3 kW负载10.32560.013910000.01420.013787.6010.859614.65980.0144
    混合负载29.85790.040510000.04350.041281.2030.215644.26980.0412
    负载条件准确率/%诊断方差
    本文方法ELMPSO-ELMSVMSSA-SVM本文方法ELMPSO-ELMSVMSSA-SVM
    空载95.602.356882.53200.023291.600.215620.30120.03621000
    1.5 kW负载96.402.958683.23650.021094.000.269820.25620.03591000
    3 kW负载95.602.478882.45620.023493.200.256820.25640.03621000
    混合负载95.607.8964247.60200.069489.60.754960.65260.10891000
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-07-06
  • 录用日期:  2021-11-14
  • 网络出版日期:  2022-01-04
  • 整期出版日期:  2023-05-31

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