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电源车传感器故障检测和数据重构方法

蒋栋年 把余江 李炜

蒋栋年,把余江,李炜. 电源车传感器故障检测和数据重构方法[J]. 北京航空航天大学学报,2023,49(7):1583-1592 doi: 10.13700/j.bh.1001-5965.2021.0501
引用本文: 蒋栋年,把余江,李炜. 电源车传感器故障检测和数据重构方法[J]. 北京航空航天大学学报,2023,49(7):1583-1592 doi: 10.13700/j.bh.1001-5965.2021.0501
JIANG D N,BA Y J,LI W. Sensor fault detection and data reconstruction method of power supply vehicle[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(7):1583-1592 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0501
Citation: JIANG D N,BA Y J,LI W. Sensor fault detection and data reconstruction method of power supply vehicle[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(7):1583-1592 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0501

电源车传感器故障检测和数据重构方法

doi: 10.13700/j.bh.1001-5965.2021.0501
基金项目: 国家自然科学基金(62263020); 甘肃省杰出青年科学基金(20JR10RA202);兰州理工大学红柳优秀青年人才资助计划;兰州市科技计划(2022-2-69)
详细信息
    通讯作者:

    E-mail:liwei@lut.edu.cn

  • 中图分类号: TP277

Sensor fault detection and data reconstruction method of power supply vehicle

Funds: National Natural Science Foundation of China (62263020); Outstanding Youth Fund of Gansu Province (20JR10RA202); Hongliu Outstanding Young Talents Support Project of Lanzhou University of Technology; Lanzhou Science and Technology Plan (2022-2-69)
More Information
  • 摘要:

    针对电源车由于运行环境复杂而容易发生传感器故障的问题,提出了一种基于时空相关性的传感器故障检测和数据重构方法。针对单个传感器运行数据的时序关系特征,借助具有选择与遗忘机制的极限学习机(SF-ELM)建立了电源车传感器时间序列预测子模型,并据此实现对电源车传感器的故障检测;针对已检测的故障传感器,利用不同传感器之间的空间相关性,通过冗余度分析,使用改进后的互信息熵筛选出与故障传感器数据相关性较高的辅助传感器数据,实现对故障传感器失效数据的在线重构;通过仿真验证了所提方法在电源车传感器故障检测和数据重构中的可行性与有效性。

     

  • 图 1  电源车传感器故障检测和数据重构流程

    Figure 1.  Sensor fault detection and data recovery process of power supply vehicle

    图 2  信息熵V氏图

    Figure 2.  Information entropy V diagram

    图 3  电源车模块关系

    Figure 3.  Relationship of power vehicle module

    图 4  传感器正常工作时OS-ELM的预测结果及误差

    Figure 4.  Prediction results and errors of OS-ELM during normal sensor operation

    图 5  传感器正常工作时SF-ELM的预测结果及误差

    Figure 5.  Prediction results and errors of SF-ELM during normal sensor operation

    图 6  传感器发生恒偏差故障时的预测结果及残差

    Figure 6.  Prediction results and residuals in case of constant bias fault of sensor

    图 7  重构误差箱线图和频域曲线

    Figure 7.  Reconstruction error box line plots and frequency domain curves

    图 8  未进行辅助变量筛选时ELM重构结果及重构误差

    Figure 8.  ELM reconstruction results and reconstruction errors without auxiliary variable screening

    图 9  辅助变量筛选前后ELM的重构结果及重构误差

    Figure 9.  Reconstruction results and reconstruction errors of ELM before and after auxiliary variable screening

    表  1  时间序列预测模型测试结果

    Table  1.   Test results of time series prediction model

    方法初始训练样本相对误差/%时间/s
    OS-ELM20010.150.95464
    SF-ELM2001.010.85689
    下载: 导出CSV

    表  2  电源车传感器之间的互信息熵

    Table  2.   Mutual information entropy between power vehicle sensors

    参数有功功率无功功率功率因子定子电流定子电压转子速度励磁电压
    有功功率1.3201×10−43.6484×10−51.3473×10−41.5541×10−43.1343×10−63.3021×10−5
    无功功率1.3201×10−43.1738×10−45.1552×10−51.3239×10−42.5767×10−52.6637×10−5
    功率因子3.6484×10−53.1738×10−41.2910×10−43.9005×10−47.3856×10−58.5718×10−5
    定子电流1.3473×10−45.1552×10−51.2910×10−41.9872×10−43.0838×10−51.0739×10−4
    定子电压1.5541×10−41.3239×10−43.9005×10−41.9872×10−41.0635×10−41.1331×10−4
    转子速度3.1343×10−62.5767×10−57.3856×10−53.0838×10−51.0635×10−41.2210×10−6
    励磁电压3.3021×10−52.6637×10−58.5718×10−51.0739×10−41.1331×10−41.2210×10−6
    下载: 导出CSV

    表  3  不同辅助变量个数的重构结果对比

    Table  3.   Comparison of reconstruction results with different number of auxiliary variables

    k相对误差/%时间/s
    40.140.87169
    60.171.02634
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-08-30
  • 录用日期:  2021-11-20
  • 网络出版日期:  2021-12-15
  • 整期出版日期:  2023-07-31

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