留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于稀疏核增量超限学习机的机载设备在线状态预测

张伟 许爱强 高明哲

张伟, 许爱强, 高明哲等 . 基于稀疏核增量超限学习机的机载设备在线状态预测[J]. 北京航空航天大学学报, 2017, 43(10): 2089-2098. doi: 10.13700/j.bh.1001-5965.2016.0802
引用本文: 张伟, 许爱强, 高明哲等 . 基于稀疏核增量超限学习机的机载设备在线状态预测[J]. 北京航空航天大学学报, 2017, 43(10): 2089-2098. doi: 10.13700/j.bh.1001-5965.2016.0802
ZHANG Wei, XU Aiqiang, GAO Mingzheet al. Online condition prediction of avionic devices based on sparse kernel incremental extreme learning machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(10): 2089-2098. doi: 10.13700/j.bh.1001-5965.2016.0802(in Chinese)
Citation: ZHANG Wei, XU Aiqiang, GAO Mingzheet al. Online condition prediction of avionic devices based on sparse kernel incremental extreme learning machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(10): 2089-2098. doi: 10.13700/j.bh.1001-5965.2016.0802(in Chinese)

基于稀疏核增量超限学习机的机载设备在线状态预测

doi: 10.13700/j.bh.1001-5965.2016.0802
基金项目: 

国家自然科学基金 61571454

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

Online condition prediction of avionic devices based on sparse kernel incremental extreme learning machine

Funds: 

National Natural Science Foundation of China 61571454

More Information
  • 摘要:

    为实现对机载设备工作状态的在线状态预测,提出了一种稀疏核增量超限学习机(ELM)算法。针对核在线学习中核矩阵膨胀问题,基于瞬时信息测量提出了一个融合构造与修剪策略的两步稀疏化方法。通过在构造阶段最小化字典冗余,在修剪阶段最大化字典元素的瞬时条件自信息量,选择一个具有固定记忆规模的稀疏字典。针对基于核的增量超限学习机核权重更新问题,提出改进的减样学习算法,其可以实现字典中任一个核函数删除后剩余核函数Gram矩阵的逆矩阵的前向递推更新。通过对某型飞机发动机的状态预测,在预测数据长度等于20的条件下,本文提出的算法将预测的整体平均误差率下降到2.18%,相比于3种流形的核超限学习机在线算法,预测精度分别提升了0.72%、0.14%和0.13%。

     

  • 图 1  核矩阵Gt的变化过程

    Figure 1.  Growing size of kernel matrices Gt

    图 2  Mackey-Glass时间序列学习的样本数量

    Figure 2.  Number of learned samples for Mackey-Glass time series

    图 3  Mackey-Glass时间序列预测曲线

    Figure 3.  Prediction curves for Mackey-Glass time series

    图 4  排气温度预测曲线

    Figure 4.  Prediction curves of exhaust gas temperature

    图 5  排气温度预测误差曲线

    Figure 5.  Prediction error curves of exhaust gas temperature

    图 6  排气温度学习的样本数量

    Figure 6.  Number of learned samples for exhaust gas temperature

    表  1  验1选择的参数设置

    Table  1.   Selected parameter setting in Experiment 1

    算法 正则化参数γ 核参数θ 其他参数
    ReOS-ELM 2×103 L=80
    KB-IELM 2×103 2×102
    SKIELM 2×103 2×102 m=80
      注:L为ReOS-ELM中隐层神经元个数。
    下载: 导出CSV

    表  2  Mackey-Glass时间序列预测结果

    Table  2.   Prediction results for Mackey-Glass time series

    算法 训练 测试
    训练时间/s RMSE RMSE MPE AER/%
    ReOS-ELM 1.062 5 0.039 3 0.036 8 0.089 7 1.38
    KB-IELM 38.935 0 0.012 6 0.011 7 0.027 6 0.98
    SKIELM 0.502 0 0.015 3 0.014 5 0.031 2 1.14
    下载: 导出CSV

    表  3  实验2选择的参数设置

    Table  3.   Selected parameter settings in Experiment 2

    项目 FOKELM ALD-KOS-ELM OKELM SKIELM
    θ m θ σ θ m θ m
    发动机扭矩 5×104 30 5×104 2×10-5 5×104 30 5×104 30
    发动机转速 1×109 30 1×109 2×10-8 1×109 30 1×109 30
    排气温度 1×107 30 1×107 2×10-9 1×107 30 1×107 30
    滑油温度 2×105 30 2×105 2×10-9 2×105 30 2×105 30
    滑油压力 2×104 30 2×104 2×10-9 2×104 30 2×104 30
    燃油瞬时流量 2×105 30 2×105 2×10-6 2×105 30 2×105 30
      注:σ为ALD-KOS-ELM的阈值参数;m为其他3种算法的时间窗宽度。
    下载: 导出CSV

    表  4  飞机发动机扭矩状态预测结果

    Table  4.   Condition prediction results for torque of aeroengine

    算法 训练 测试
    训练时间/
    s
    RMSE/
    (N·m)
    RMSE/
    (N·m)
    MPE/
    (N·m)
    AER/
    %
    FOKELM 0.034 0 1.016 7 0.949 6 2.044 4 10.87
    ALD-KOS-ELM 0.034 5 0.839 2 0.955 4 2.581 4 9.84
    OKELM 0.034 4 0.782 2 0.962 9 2.655 6 10.09
    SKIELM 0.038 0 0.752 8 0.929 2 2.578 5 9.76>
    下载: 导出CSV

    表  5  飞机发动机转速状态预测结果

    Table  5.   Condition prediction results for rotational speed of aeroengine

    算法 训练 测试
    训练
    时间/s
    RMSE
    (r·
    min-1)
    RMSE/
    (r·
    min-1)
    MPE/
    (r·
    min-1)
    AER/
    %
    FOKELM 0.036 6 98.919 6 68.621 0 157.50 0.22
    ALD-KOS-ELM 0.030 7 92.516 0 77.050 0 173.71 0.22
    OKELM 0.032 9 94.846 1 66.927 4 149.73 0.27
    SKIELM 0.035 9 88.533 8 64.282 1 149.02 0.19
    下载: 导出CSV

    表  6  飞机发动机排气温度状态预测结果

    Table  6.   Condition prediction results for exhaust gas temperature of aeroengine

    算法 训练 测试
    训练时间/s RMSE/K RMSE/K MPE/K AER/%
    FOKELM 0.034 9 5.273 6 2.632 1 4.860 8 0.49
    ALD-KOS-ELM 0.065 2 3.626 4 2.840 6 5.664 6 0.49
    OKELM 0.038 8 3.929 0 3.131 5 7.274 9 0.55
    SKIELM 0.027 6 3.481 7 2.495 3 5.286 9 0.47
    下载: 导出CSV

    表  7  飞机发动机滑油温度状态预测结果

    Table  7.   Condition prediction results for oil temperature of aeroengine

    算法 训练 测试
    训练时间/s RMSE/℃ RMSE/℃ MPE/℃ AER/%
    FOKELM 0.023 6 0.167 6 0.203 8 0.310 6 0.52
    ALD-KOS-ELM 0.072 1 0.031 9 0.086 3 0.138 2 0.21
    OKELM 0.031 5 0.025 3 0.059 3 0.100 3 0.14
    SKIELM 0.031 3 0.026 0 0.059 2 0.100 0 0.14
    下载: 导出CSV

    表  8  飞机发动机滑油压力状态预测结果

    Table  8.   Condition prediction results for oil pressure of aeroengine

    算法 训练 测试
    训练时间/s RMSE/N RMSE/N MPE/N AER/%
    FOKELM 0.027 5 0.097 0 0.108 8 0.127 1 3.51
    ALD-KOS-ELM 0.063 7 0.039 6 0.033 7 0.043 6 1.05
    OKELM 0.033 0 0.036 1 0.029 7 0.038 6 0.92
    SKIELM 0.026 3 0.034 3 0.024 8 0.033 2 0.75
    下载: 导出CSV

    表  9  飞机发动机燃油瞬时流量状态预测结果

    Table  9.   Condition prediction results for fuel instantaneous flux of aeroengine

    算法 训练 测试
    训练
    时间/s
    RMSE/(L·
    min-1)
    RMSE/(L·
    min-1)
    MPE/(L·
    min-1)
    AER/
    %
    FOKELM 0.028 5 2.635 8 6.523 2 22.729 7 1.89
    ALD-KOS-ELM 0.019 6 3.118 3 6.564 2 18.451 2 2.13
    OKELM 0.031 4 2.707 2 6.547 8 22.688 6 1.94
    SKIELM 0.027 6 2.567 2 6.100 5 22.327 9 1.82
    下载: 导出CSV
  • [1] TIAN Z, QIAN C, GU B, et al.Electric vehicle air conditioning system performance prediction based on artificial neural network[J].Applied Thermal Engineering, 2015, 89:101-104. doi: 10.1016/j.applthermaleng.2015.06.002
    [2] 孙伟超, 李文海, 李文峰.融合粗糙集与D-S证据理论的航空装备故障诊断[J].北京航空航天大学学报, 2015, 41(10):1902-1909. http://bhxb.buaa.edu.cn/CN/abstract/abstract13502.shtml

    SUN W C, LI W H, LI W F.Avionic devices fault diagnosis based on fusion method of rough set and D-S theory[J].Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(10):1902-1909(in Chinese). http://bhxb.buaa.edu.cn/CN/abstract/abstract13502.shtml
    [3] YE F M, ZHANG Z B, CHAKRABARTY K, et al.Board-level functional fault diagnosis using multikernel support vector machines and incremental learning[J].IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems, 2014, 33(2):279-290. doi: 10.1109/TCAD.2013.2287184
    [4] JIE Y.A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes[J].Chemical Engineering Science, 2012, 68(1):506-519. doi: 10.1016/j.ces.2011.10.011
    [5] ZHAO X Q, XUE Y F, WANG T.Fault detection of batch process based on multi-way kernel T-PLS[J].Journal of Chemical and Pharmaceutical Research, 2014, 6(7):338-346.
    [6] HUANG G B, ZHOU H, DING X, et al.Extreme learning machine for regression and multiclass classification[J].IEEE Transactions on Systems, Man and Cybernetics-Part B:Cybernetics, 2011, 42(2):513-529. http://www.ntu.edu.sg/home/egbhuang/pdf/ELM-Unified-Learning.pdf
    [7] HUANG G B, ZHU Q Y, SIEW C K.Extreme learning machine:Theory and application[J].Neurocomputing, 2006, 70(1-3):489-501. doi: 10.1016/j.neucom.2005.12.126
    [8] GUO L, HAO J H, LIU M.An incremental extreme learning machine for online sequential learning problems[J].Neurocomputing, 2014, 128:50-58. doi: 10.1016/j.neucom.2013.03.055
    [9] ZHAO S L, CHEN B D, ZHU P P, et al.Fixed budget quantized kernel least-mean-square algorithm[J].Signal Processing, 2013, 93(9):2759-2770. doi: 10.1016/j.sigpro.2013.02.012
    [10] RICHARD C, BERMUDEZ M, HONEINE P.Online prediction of time series data with kernels[J].IEEE Transactions on Signal Processing, 2009, 57(3):1058-1067. doi: 10.1109/TSP.2008.2009895
    [11] GAO W, CHEN J, RICHARD C, et al.Online dictionary learning for kernel LMS[J].IEEE Transactions on Signal Processing, 2014, 62(11):2765-2777. doi: 10.1109/TSP.2014.2318132
    [12] FAN H J, SONG Q, XU Z.Online learning with kernel regularized least mean square algorithms[J].Knowledge-Based Systems, 2014, 59:21-32. doi: 10.1016/j.knosys.2014.02.005
    [13] DIETHE T, GIROLAMI M.Online learning with (multiple) kernels:A review[J].Neural Computation, 2013, 25(3):567-625. doi: 10.1162/NECO_a_00406
    [14] HONEINE P.Analyzing sparse dictionaries for online learning with kernels[J].IEEE Transactions on Signal Processing, 2015, 63(23):6343-6353. doi: 10.1109/TSP.2015.2457396
    [15] ENGEL Y, MANNOR S, MEIR R.The kernel recursive least-squares algorithm[J].IEEE Transactions on Signal Processing, 2004, 52(8):2275-2285. doi: 10.1109/TSP.2004.830985
    [16] LIU W F, PARK I, PRINCIPE J C.An information theoretic approach of designing sparse kernel adaptive filters[J].IEEE Transactions on Neural Networks, 2009, 20(12):1950-1961. doi: 10.1109/TNN.2009.2033676
    [17] ZHOU X R, LIU Z J, ZHU C X.Online regularized and kernelized extreme learning machines with forgetting mechanism[J].Mathematical problems in engineering, 2014, 2014:1-11. doi: 10.1007/s13042-017-0666-8
    [18] ZHOU X R, WANG C H.Cholesky factorization based online regularized and kernelized extreme learning machines with forgetting mechanism[J].Neurocomputing, 2016, 174:1147-1155. doi: 10.1016/j.neucom.2015.10.033
    [19] GU Y, LIU J F, CHEN Y Q, et al.TOSELM:Timeliness online sequential extreme learning machin[J].Neurocomputing, 2014, 128:119-127. doi: 10.1016/j.neucom.2013.02.047
    [20] SIMONE S, DANILO C, MICHELE S, et al.Online sequential extreme learning machine with kernel[J].IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(9):2214-2220. doi: 10.1109/TNNLS.2014.2382094
    [21] 张英堂, 马超, 李志宁, 等.基于快速留一交叉验证的核极限学习机在线建模[J].上海交通大学学报, 2014, 48(5):641-646. http://www.cnki.com.cn/Article/CJFDTOTAL-SHJT201405011.htm

    ZHANG Y T, MA C, LI Z N, et al.Online modeling of kernel extreme learning machine based on fast leave-one-out cross-validation[J].Journal of Shanghai Jiaotong University, 2014, 48(5):641-646(in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-SHJT201405011.htm
    [22] HUYNH H T, WON Y.Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks[J].Pattern Recognition Letters, 2011, 32(14):1930-1935. doi: 10.1016/j.patrec.2011.07.016
  • 加载中
图(6) / 表(9)
计量
  • 文章访问数:  519
  • HTML全文浏览量:  0
  • PDF下载量:  476
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-10-17
  • 录用日期:  2016-10-28
  • 刊出日期:  2017-10-20

目录

    /

    返回文章
    返回
    常见问答