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基于近邻传播聚类的航空电子部件LMK诊断模型

张伟 许爱强 平殿发 夏菲

张伟, 许爱强, 平殿发, 等 . 基于近邻传播聚类的航空电子部件LMK诊断模型[J]. 北京航空航天大学学报, 2018, 44(8): 1693-1704. doi: 10.13700/j.bh.1001-5965.2017.0632
引用本文: 张伟, 许爱强, 平殿发, 等 . 基于近邻传播聚类的航空电子部件LMK诊断模型[J]. 北京航空航天大学学报, 2018, 44(8): 1693-1704. doi: 10.13700/j.bh.1001-5965.2017.0632
ZHANG Wei, XU Aiqiang, PING Dianfa, et al. Localized multi-kernel diagnosis model for avionics based on affinity propagation clustering[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(8): 1693-1704. doi: 10.13700/j.bh.1001-5965.2017.0632(in Chinese)
Citation: ZHANG Wei, XU Aiqiang, PING Dianfa, et al. Localized multi-kernel diagnosis model for avionics based on affinity propagation clustering[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(8): 1693-1704. doi: 10.13700/j.bh.1001-5965.2017.0632(in Chinese)

基于近邻传播聚类的航空电子部件LMK诊断模型

doi: 10.13700/j.bh.1001-5965.2017.0632
基金项目: 

国家自然科学基金 61571454

山东省自然科学基金 ZR2016FQ03

详细信息
    作者简介:

    张伟  男, 博士研究生。主要研究方向:机载电子设备状态监测与故障诊断

    许爱强  男, 博士, 教授, 博士生导师。主要研究方向:复杂电子系统自动测试与诊断技术

    通讯作者:

    许爱强, E-mail:hjhyautotest@sina.com

  • 中图分类号: V243;TP181

Localized multi-kernel diagnosis model for avionics based on affinity propagation clustering

Funds: 

National Natural Science Foundation of China 61571454

Natural Science Foundation of Shandong Province, China ZR2016FQ03

More Information
  • 摘要:

    针对小样本条件下,航空电子部件功能模块故障诊断精度不高的问题,将局部多核学习(LMKL)算法的多分辨率解释与局部特征自适应表示能力和超限学习机(ELM)运算高效的特点相结合,提出一种新的局部聚类MK-ELM(LCMKELM)诊断模型。通过引入近邻传播(AP)聚类,在挖掘训练样本局部特征信息的同时,有效约减了局部算法的计算复杂性,避免了过学习问题的出现;通过分别分析输入空间与特征空间的聚类特征,构造了相应的2种选通函数M1M2,以优化选通函数的模型参数取代优化局部权重,有效解决了核超限学习机(KELM)的对偶优化形式关于局部权重二次非凸的问题。将本文模型应用于某型机旋转变压器激励发生电路功能模块故障诊断,结果表明:相比于4种常用的多核诊断算法,模型在实现低漏警、低虚警的同时,采用M1选通函数的诊断算法将诊断精度平均提升了3.80%,采用M2选通函数的诊断算法将诊断精度平均提升了5.98%。同时,模型在实现与流行的LMKL算法相近的训练时间的同时,测试时间更短。

     

  • 图 1  本文模型的基本框架

    Figure 1.  General framework of proposed model

    图 2  本文模型的决策过程

    Figure 2.  Decision-making process of proposed model

    图 3  本文故障诊断模型流程图

    Figure 3.  Flowchart of proposed fault diagnosis model

    图 4  Gauss 4训练样本AP算法聚类结果

    Figure 4.  Clustering results of Gauss 4 training samples based on AP algorithm

    图 5  Gauss 4数据集ROC曲线比较

    Figure 5.  Comparison of ROC curves on Gauss 4 dataset

    图 6  旋转变压器激励发生电路原理图

    Figure 6.  Schematic diagram of rotary transformer excitation generating circuit

    图 7  不同算法的混淆矩阵

    Figure 7.  Confusion matrices of different algorithms

    图 8  本文算法局部权重分布

    Figure 8.  Localized weight distribution of proposed algorithms

    图 9  不同算法的召回率、准确率比较

    Figure 9.  Comparison of recall and precision based on different algorithms

    表  1  Gauss 4数据集分类结果比较

    Table  1.   Comparison of classification results on Gauss 4 dataset

    评价指标 M1-LCMKELM M2-LCMKELM SimpleMKL
    分类精度/% 90.500 0±0.684 7 90.850 0±0.602 1 89.350 0±1.206 8
    F1值 0.904 9±0.006 8 0.908 5±0.006 1 0.895 8±0.012 4
    G-mean 0.904 6±0.006 9 0.908 2±0.006 2 0.893 2±0.011 9
    下载: 导出CSV

    表  2  不同训练样本数量下分类精度比较

    Table  2.   Comparison of classification accuracy with different sizes of training sample

    训练样本数量 分类精度/%
    M1-LCMKELM M2-LCMKELM
    16 81.8±1.923 5 82.6±1.673 3
    32 85.2±1.483 2 86.2±1.483 2
    48 86.4±2.073 6 86.6±1.516 5
    64 86.4±1.516 5 86.6±1.673 3
    144 87.2±1.095 4 87.4±1.140 2
    400 87.0±1.870 8 87.2±1.303 8
    600 87.8±1.095 4 88.2±0.836 7
    800 88.0±0.707 1 88.6±0.894 4
    下载: 导出CSV

    表  3  诊断数据聚类结果

    Table  3.   Clustering results of diagnosis data

    聚类 聚类中心 类内元素
    1 5 {2, 4, 5, 6, 9, 11, 15, 18, 34, 35}
    2 12 {8, 10, 12, 13, 20, 25}
    3 17 {1, 3, 7, 17}
    4 23 {16, 23, 33}
    5 26 {14, 19, 26, 29, 32}
    6 30 {24, 30, 31, 41}
    7 40 {21, 22, 28, 37, 38, 40, 42, 45}
    8 43 {27, 36, 39, 43, 44, 46}
    下载: 导出CSV

    表  4  不同算法诊断结果比较

    Table  4.   Comparison of diagnosis results based on different algorithms

    算法 漏警率/
    %
    虚警率/
    %
    训练诊断精度/% 测试诊断精度/%
    SimpleMKL 2.857 1 0 100 91.304 3
    GMKL-SVM 0 0 100 93.478 3
    LMKL-softmax 0 5.405 4 100 89.130 4
    LMKL-sigmoid 2.857 1 0 100 93.478 3
    M1-LCMKELM 0 0 100 95.652 2
    M2-LCMKELM 0 0 100 97.826 1
    下载: 导出CSV

    表  5  不同算法F1值和G-mean比较

    Table  5.   Comparison of F1 score and G-mean based on different algorithms

    算法 F1值 G-mean
    SimpleMKL 0.898 7 0.882 6
    GMKL-SVM 0.920 8 0.910 3
    LMKL-softamx 0.885 5 0.874 5
    LMKL-sigmoid 0.924 3 0.910 3
    M1-LCMKELM 0.944 4 0.934 9
    M2-LCMKELM 0.972 9 0.969 6
    下载: 导出CSV

    表  6  不同算法诊断时间花费比较

    Table  6.   Comparison of diagnosis time cost based on different algorithms

    算法 训练时间/s 测试时间/s
    SimpleMKL 0.991 7±0.082 9 0.201 8±0.045 1
    GMKL-SVM 0.832 5±0.036 0 0.167 9±0.008 2
    LMKL-softamx 1.493 4±0.062 5 0.173 5±0.011 1
    LMKL-sigmoid 2.715 9±0.129 2 0.176 9±0.015 7
    M1-LCMKELM 2.497 9±0.213 7 0.129 9±0.005 9
    M2-LCMKELM 1.789 7±0.201 4 0.152 1±0.016 7
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-10-16
  • 录用日期:  2017-11-17
  • 刊出日期:  2018-08-20

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