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基于改进结构保持数据降维方法的故障诊断研究

韩敏 李宇 韩冰

韩敏, 李宇, 韩冰.基于改进结构保持数据降维方法的故障诊断研究.自动化学报, 2021, 47(2): 338-348 doi: 10.16383/j.aas.c180138
引用本文: 韩敏, 李宇, 韩冰.基于改进结构保持数据降维方法的故障诊断研究.自动化学报, 2021, 47(2): 338-348 doi: 10.16383/j.aas.c180138
Han Min, Li Yu, Han Bing. Research on fault diagnosis of data dimension reduction based on improved structure preserving algorithm. Acta Automatica Sinica, 2021, 47(2): 338-348 doi: 10.16383/j.aas.c180138
Citation: Han Min, Li Yu, Han Bing. Research on fault diagnosis of data dimension reduction based on improved structure preserving algorithm. Acta Automatica Sinica, 2021, 47(2): 338-348 doi: 10.16383/j.aas.c180138

基于改进结构保持数据降维方法的故障诊断研究

doi: 10.16383/j.aas.c180138
基金项目: 

国家自然科学基金 61773087

中央高校基本科研业务费 DUT17ZD216

上海启明星 15QB1400800

详细信息
    作者简介:

    李宇   大连理工大学电子信息与电气工程学部硕士研究生.主要研究方向为柴油机故障诊断技术. E-mail: liyu0512@mail.dlut.edu.cn

    韩冰   上海船舶航运研究院航海与安全技术国家重点实验室研究员.主要研究方向为船舶动力平台故障诊断和故障预测. E-mail: hanbing@sssri.com

    通讯作者:

    韩敏   大连理工大学电子信息与电气工程学部教授.主要研究方向为模式识别, 复杂系统建模与分析及时间序列预测.本文通信作者. E-mail: minhan@dlut.edu.cn

  • 本文责任编委 曾志刚

Research on Fault Diagnosis of Data Dimension Reduction Based on Improved Structure Preserving Algorithm

Funds: 

National Natural Science Foundation of China 61773087

Fundamental Research Funds for the Central Universities DUT17ZD216

Shanghai Rising-Star Program 15QB1400800

More Information
    Author Bio:

    LI Yu   Master student at the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. His main research interest is diesel engine fault diagnosis

    HAN Bing   Researcher at the State Key Laboratory of Navigation and Safety Technology, Shanghai Ship and Shipping Research Institute. His research interest covers fault diagnosis and prognostic of ship power plant

    Corresponding author: HAN Min    Professor at the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. Her research interest covers pattern recognition, modeling and analysis of complex system, and time series prediction. Corresponding author of this paper
  • Recommended by Associate Editor ZENG Zhi-Gang
  • 摘要: 传统基于核主成分分析(Kernel principal component analysis, KPCA)的数据降维方法在提取有效特征信息时只考虑全局结构保持而未考虑样本间的局部近邻结构保持问题, 本文提出一种改进全局结构保持算法的特征提取与降维方法.改进的特征提取与降维方法将流形学习中核局部保持投影(Kernel locality preserving projection, KLPP)的思想融入核主成分分析的目标函数中, 使样本投影后的特征空间不仅保持原始样本空间的整体结构, 还保持样本空间相似的局部近邻结构, 包含更丰富的特征信息.上述方法通过同时进行的正交化处理可避免局部子空间结构发生失真, 并能够直观显示出低维结果, 将低维数据输入最近邻分类器, 以识别率和聚类分析结果作为衡量指标, 同时将所提方法应用于故障诊断中.使用AVL Boost软件模拟的柴油机故障数据和田纳西(Tennessee Eastman, TE)化工数据仿真, 验证了所提方法的有效性.
    Recommended by Associate Editor ZENG Zhi-Gang
    1)  本文责任编委 曾志刚
  • 图  1  数据降维方法流程

    Fig.  1  The dimension reduction process of data

    图  2  柴油机模型仿真

    Fig.  2  The diesel engine simulation

    图  3  不同维度贡献率统计图

    Fig.  3  The contribution rate of different dimensions

    图  4  KPCA降维结果

    Fig.  4  The dimension reduction based on KPCA

    图  5  KLPP降维结果

    Fig.  5  The dimension reduction based on KLPP

    图  6  KFDA降维结果

    Fig.  6  The dimension reduction based on KFDA

    图  7  LGPCA降维结果

    Fig.  7  The dimension reduction based on LGPCA

    图  8  TGLSA降维结果

    Fig.  8  The dimension reduction based on TGLSA

    图  9  GLSP降维结果

    Fig.  9  The dimension reduction based on GLSP

    图  10  六类算法降维效果衡量指标

    Fig.  10  The dimension reduction performance of 6 methods

    图  11  KPCA算法降维效果

    Fig.  11  The dimension reduction performance of KPCA on TE data

    图  12  TE数据下KLPP算法降维效果

    Fig.  12  The dimension reduction performance of KLPP on TE data

    图  13  TE数据下KFDA算法降维效果

    Fig.  13  The dimension reduction performance of KLPP on TE data

    图  14  TE数据下LGPCA算法降维效果

    Fig.  14  The dimension reduction performance of LGPCA on TE data

    图  15  TE数据下TGLSA算法降维效果

    Fig.  15  The dimension reduction performance of TGLSA on TE data

    图  16  TE数据下GLSP算法降维效果

    Fig.  16  The dimension reduction performance of GLSP on TE data

    表  1  正常工况与故障工况模拟

    Table  1  The simulation of normal and fault conditions

    No. 工况类型 样本个数 数据维数
    1 正常工况 960 15
    2 故障1_空冷器冷却不足 960 15
    3 故障2_排气口堵塞 960 15
    4 故障3_涡轮增压效率降低 960 15
    下载: 导出CSV

    表  2  数据与台架实验数据多工况对比

    Table  2  The data contrast between AVL Boost and bench test under multiple working conditions

    负荷 排气温度(℃) 相对误差(%) 功率(kW) 相对误差(%)
    模型数据 台架实验数据 模型数据 台架实验数据
    90%负荷 329.89 328.50 0.42 3 281.40 3 277.00 0.13
    75%负荷 304.39 307.30 0.95 2 839.20 2 844.00 0.17
    75%推进 319.23 320.90 0.37 2 866.85 2 864.00 0.10
    下载: 导出CSV

    表  3  故障1识别准确率($ \% $)

    Table  3  The accuracy of fault1 diagnosis ($ \% $)

    方法 Fault1
    KPCA KLPP KFDA LGPCA TGLSA GLSP
    ELM 55.32 61.38 60.58 54.21 58.69 62.97
    SVM 58.69 70.61 71.68 65.34 68.49 69.27
    RVM 72.77 69.59 74.21 68.98 63.40 76.35
    KNN 72.26 66.86 70.38 75.49 77.36 78.53
    下载: 导出CSV

    表  4  故障2识别准确率($ \% $)

    Table  4  The accuracy of fault2 diagnosis ($ \% $)

    方法 Fault2
    KPCA KLPP KFDA LGPCA TGLSA GLSP
    ELM 80.95 76.85 79.65 77.49 70.28 82.62
    SVM 78.36 77.32 77.05 74.39 72.15 80.09
    RVM 79.74 74.16 78.66 85.68 81.29 83.62
    KNN 82.35 82.63 75.39 78.91 86.54 88.84
    下载: 导出CSV

    表  5  故障3识别准确率($ \% $)

    Table  5  The accuracy of fault3 diagnosis ($ \% $)

    方法 Fault3
    KPCA KLPP KFDA LGPCA TGLSA GLSP
    ELM 70.65 72.39 77.16 74.29 70.53 79.26
    SVM 66.34 68.29 68.49 65.39 60.87 66.58
    RVM 59.38 62.58 55.21 59.86 60.13 66.34
    KNN 58.62 62.38 65.98 63.24 61.09 65.08
    下载: 导出CSV

    表  6  特征提取所需时间(s)

    Table  6  Feature extraction time (s)

    维度 特征提取方法
    KPCA KLPP KFDA LGPCA TGLSA GLSP
    3 0.651 1.155 1.039 2.598 2.134 1.596
    5 0.795 1.159 1.118 2.019 1.495 1.632
    8 0.815 1.209 0.975 1.069 1.396 1.885
    10 0.867 1.344 1.185 1.563 2.098 1.962
    下载: 导出CSV
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  • 收稿日期:  2018-03-09
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