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基于LSTM循环神经网络的故障时间序列预测

王鑫 吴际 刘超 杨海燕 杜艳丽 牛文生

王鑫, 吴际, 刘超, 等 . 基于LSTM循环神经网络的故障时间序列预测[J]. 北京航空航天大学学报, 2018, 44(4): 772-784. doi: 10.13700/j.bh.1001-5965.2017.0285
引用本文: 王鑫, 吴际, 刘超, 等 . 基于LSTM循环神经网络的故障时间序列预测[J]. 北京航空航天大学学报, 2018, 44(4): 772-784. doi: 10.13700/j.bh.1001-5965.2017.0285
WANG Xin, WU Ji, LIU Chao, et al. Exploring LSTM based recurrent neural network for failure time series prediction[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(4): 772-784. doi: 10.13700/j.bh.1001-5965.2017.0285(in Chinese)
Citation: WANG Xin, WU Ji, LIU Chao, et al. Exploring LSTM based recurrent neural network for failure time series prediction[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(4): 772-784. doi: 10.13700/j.bh.1001-5965.2017.0285(in Chinese)

基于LSTM循环神经网络的故障时间序列预测

doi: 10.13700/j.bh.1001-5965.2017.0285
基金项目: 

中国民用航空专项研究项目 MJ-S-2013-10

国防科工局技术基础项目 JSZL2014601B008

国家自然科学基金 61602237

详细信息
    作者简介:

    王鑫  男, 博士研究生。主要研究方向:数据驱动技术

    吴际  男, 博士, 副教授, 硕士生导师。主要研究方向:模型驱动、软件可靠性分析

    刘超  男, 博士, 教授, 博士生导师。主要研究方向:软件工程、软件测试

    通讯作者:

    吴际, E-mail: wuji@buaa.edu.cn

  • 中图分类号: O213.2;V37;TP18

Exploring LSTM based recurrent neural network for failure time series prediction

Funds: 

China Civil Aviation Special Research Project MJ-S-2013-10

Technology Foundation Program of the National Defense Technology Industry Ministry JSZL2014601B008

National Natural Science Foundation of China 61602237

More Information
  • 摘要:

    有效地预测使用阶段的故障数据对于合理制定可靠性计划以及开展可靠性维护活动等具有重要的指导意义。从复杂系统的历史故障数据出发,提出了一种基于长短期记忆(LSTM)循环神经网络的故障时间序列预测方法,包括网络结构设计、网络训练和预测过程实现算法等,进一步以预测误差最小为目标,提出了一种基于多层网格搜索的LSTM预测模型参数优选算法,通过与多种典型时间序列预测模型的实验对比,验证了所提出的LSTM预测模型及其参数优选算法在故障时间序列分析中具有很强的适用性和更高的准确性。

     

  • 图 1  RNN模型及隐藏层细胞结构

    Figure 1.  RNN model and cell structure in hidden layer

    图 2  LSTM隐藏层细胞结构

    Figure 2.  LSTM cell structure in hidden layer

    图 3  基于LSTM的故障时间序列预测框架

    Figure 3.  LSTM based framework for failure time series prediction

    图 4  A、B飞机的月度故障时间序列数据

    Figure 4.  Monthly failure time series data for Aircraft A and Aircraft B

    图 5  不同学习率的损失变化和模型精度对比(A飞机)

    Figure 5.  Comparison of loss change and model accuracy with different learning rates (Aircraft A)

    图 6  不同隐藏层细胞的损失变化和模型精度对比(学习率η=0.1,A飞机)

    Figure 6.  Comparison of loss change and model accuracy with different hidden layer cells (learning rate η=0.1, Aircraft A)

    图 7  不同学习率的损失变化和模型精度对比(B飞机)

    Figure 7.  Comparison of loss change and model accuracy with different learning rates (Aircraft B)

    图 8  不同隐藏层细胞的损失变化和模型精度对比(学习率η=0.03,B飞机)

    Figure 8.  Comparison of loss change and model accuracy with different hidden layer cells (learning rate η=0.03, Aircraft B)

    图 9  LSTM模型的拟合和预测结果(学习率η=0.03,B飞机)

    Figure 9.  Fitting and forecasting results with LSTM model (learning rate η=0.03, Aircraft B)

    图 10  LSTM模型3参数多层网格搜索结果(A飞机)

    Figure 10.  Multilayer grid search results for three parameters of LSTM model (Aircraft A)

    图 11  LSTM模型3参数多层网格搜索结果(B飞机)

    Figure 11.  Multilayer grid search results for three parameters of LSTM model (Aircraft B)

    表  1  不同预测模型实验结果对比(A飞机)

    Table  1.   Experimental results for different prediction models (Aircraft A)

    模型 模型参数 训练集
    拟合
    RMSE值
    测试集预测RMSE值 耗时/s
    1个测试点 2个测试点 3个测试点 6个测试点 12个测试点
    Holt-Wintersa α=0.044,β=0.073,γ=0.223 2.617 0.088 0.069 2.278 2.882 2.595 0.02
    Holt-Wintersm α=0,β=0,γ=0.6 3.199 1.704 1.205 2.837 3.179 3.066 0.02
    ARIMA p=2,d=1,q=2 2.329 1.475 1.224 2.712 2.832 2.509 1.53
    SSAr Lssa=96,Gssa=list(1:50) 0.770 2.487 1.781 1.871 2.437 2.622 0.02
    SSAv Lssa=96,Gssa=list(1:50) 0.770 2.509 1.843 2.175 2.500 2.295 0.02
    MLR Lmlr=24 2.221 2.490 1.773 2.602 2.617 2.381 0.02
    SVR Lsvr=24, C=3,ε=0.259,σsvr=0.023 1.167 1.740 1.526 1.967 2.139 2.321 0.03
    RNN L=2, Sstate=6,seed=1, steps=500,η=0.1 2.183 1.716 1.225 1.995 2.528 2.595 0.61
    GRU L=2, Sstate=6,seed=1, steps=500,η=0.1 1.982 1.921 1.651 2.920 2.691 2.248 0.78
    LSTM L=2, Sstate=6,seed=1, steps=500,η=0.1 1.962 1.919 1.577 2.745 2.109 2.196 0.81
    注:最小RMSE值和最小耗时由下划线标记。
    下载: 导出CSV

    表  2  不同预测模型实验结果对比(B飞机)

    Table  2.   Experimental results for different prediction models (Aircraft B)

    模型 模型参数 训练集
    拟合
    RMSE值
    测试集预测RMSE值 耗时/s
    1个测试点 2个测试点 3个测试点 6个测试点 12个测试点
    Holt-Wintersa α=0.011, β=0.210, γ=0.191 2.907 1.749 3.605 3.353 2.609 2.474 0.02
    Holt-Wintersm α=0, β=0, γ=0.438 3.231 1.837 4.252 3.807 2.950 2.816 0.02
    ARIMA p=4,d=1,q=1 2.565 1.719 2.079 2.566 2.091 2.021 1.77
    SSAr Lssa=96,Gssa=list(1:50) 0.853 0.178 2.142 1.768 4.289 5.023 0.02
    SSAv Lssa=96,Gssa=list(1:50) 0.853 0.730 1.686 1.987 2.904 3.161 0.02
    MLR Lmlr=24 2.547 1.729 2.026 2.912 2.418 2.360 0.02
    SVR Lsvr=24, C=3,ε=0.252,σsvr=0.023 1.353 0.241 1.374 2.893 2.278 2.121 0.03
    RNN L=12, Sstate=6,seed=100, steps=1 000,η=0.03 2.058 0.828 2.630 2.556 2.484 2.671 3.13
    GRU L=12, Sstate=6,seed=100, steps=1 000,η=0.03 1.559 1.696 1.257 3.525 2.815 2.690 5.36
    LSTM L=12, Sstate=6,seed=100, steps=1 000,η=0.03 1.276 0.956 1.691 1.703 1.237 1.580 5.64
    注:最小RMSE值和最小耗时由下划线标记。
    下载: 导出CSV

    表  3  LSTM模型前5组最优参数组合以及对应的模型精度(A飞机)

    Table  3.   The first five groups of optimal parameters andcorresponding model accuracy for LSTM model (Aircraft A)

    排名 模型参数 训练集
    拟合RMSE值
    测试集预测RMSE值 耗时/s
    L Sstate η 1个测试点 2个测试点 3个测试点 6个测试点 12个测试点
    1 3 21 0.005 1.261 0.694 0.921 1.261 1.154 1.676 1.56
    2 14 10 0.03 0.321 2.539 1.834 2.506 2.390 1.909 3.63
    3 17 8 0.005 1.311 1.923 1.824 2.137 2.004 2.041 3.94
    4 19 11 0.03 0.289 0.054 0.762 1.290 2.058 2.061 4.84
    5 4 16 0.03 0.584 3.860 2.759 2.395 1.991 2.081 1.66
    下载: 导出CSV

    表  4  LSTM模型前5组最优参数组合以及对应的模型精度(B飞机)

    Table  4.   The first five groups of optimal parameters and corresponding model accuracy for LSTM model (Aircraft B)

    排名 模型参数 训练集
    拟合RMSE值
    测试集预测RMSE值 耗时/s
    L Sstate η 1个测试点 2个测试点 3个测试点 6个测试点 12个测试点
    1 10 7 0.005 1.794 1.703 1.209 1.005 0.942 0.864 2.55
    2 3 18 0.003 2.288 0.833 1.398 1.162 1.227 1.571 1.44
    3 19 18 0.005 0.945 0.495 1.804 2.206 2.093 1.636 5.95
    4 3 13 0.003 1.978 0.306 1.400 1.517 1.300 1.647 1.31
    5 3 6 0.01 2.056 0.563 1.436 1.440 1.182 1.647 0.97
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-05-08
  • 录用日期:  2017-08-11
  • 刊出日期:  2018-04-20

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