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基于集成神经网络的剩余寿命预测

张永峰 陆志强

张永峰, 陆志强. 基于集成神经网络的剩余寿命预测[J]. 工程科学学报, 2020, 42(10): 1372-1380. doi: 10.13374/j.issn2095-9389.2019.10.10.005
引用本文: 张永峰, 陆志强. 基于集成神经网络的剩余寿命预测[J]. 工程科学学报, 2020, 42(10): 1372-1380. doi: 10.13374/j.issn2095-9389.2019.10.10.005
ZHANG Yong-feng, LU Zhi-qiang. Remaining useful life prediction based on an integrated neural network[J]. Chinese Journal of Engineering, 2020, 42(10): 1372-1380. doi: 10.13374/j.issn2095-9389.2019.10.10.005
Citation: ZHANG Yong-feng, LU Zhi-qiang. Remaining useful life prediction based on an integrated neural network[J]. Chinese Journal of Engineering, 2020, 42(10): 1372-1380. doi: 10.13374/j.issn2095-9389.2019.10.10.005

基于集成神经网络的剩余寿命预测

doi: 10.13374/j.issn2095-9389.2019.10.10.005
基金项目: 国家自然科学基金资助项目(71171130,61273035)
详细信息
    通讯作者:

    E-mail: zhiqianglu@tongji.edu.cn

  • 中图分类号: TP399

Remaining useful life prediction based on an integrated neural network

More Information
  • 摘要: 针对机器或设备的剩余寿命(Remaining useful life, RUL)预测精度低的问题,提出基于一维卷积神经网络(Convolutional neural network, CNN)和双向长短期记忆(Bidirectional long short-term memory, BD-LSTM)的集成神经网络模型。为了更好地抽取时间序列上的特征,以及产生更多的训练样本,采用滑动窗口对数据进行处理,同时采用卡尔曼滤波对数据进行降噪处理,将数据标准化以及设置RUL标签。与人工提取特征不同,利用一维CNN对数据进行特征提取,并舍弃了CNN中的池化层。然后将提取到的高维特征输入到BD-LSTM进行回归预测,并采用Bagging的方式对此神经网络进行集成来预测RUL。最后通过在NASA的数据集上验证该模型的有效性,以及相比于其他机器学习或者深度学习模型的优越性,实验表明所提模型在RUL预测方面更加准确。
  • 图  1  一维CNN的操作示意图

    Figure  1.  Illustration of the one-dimensional convolutional neural network operation

    图  2  LSTM单元结构示意图

    Figure  2.  Diagram of the LSTM cell

    图  3  双向LSTM操作示意图

    Figure  3.  Diagram of the bidirectional LSTM network

    图  4  模型框架

    Figure  4.  Model framework

    图  5  不同的RUL标签对比

    Figure  5.  Comparison of different RUL labels

    图  6  预处理前后的传感器数据。(a,c)s12传感器;(b,d) s2传感器

    Figure  6.  Sensor data before and after preprocessing: (a,c) Sensor 12; (b,d) Sensor 2

    图  7  卷积层权值分布。(a)第1个卷积层;(b)第2个卷积层;(c)第3个卷积层;(d)第4个卷积层

    Figure  7.  Convolutional layer weight distribution: (a) the first convolutional layer; (b) the second convolutional layer; (c) the third convolutional layer; (d) the fourth convolutional layer

    图  8  卷积层数目对RMSE的影响

    Figure  8.  Effect of the number of convolution layers on the root–mean–square error

    图  9  不同评价函数的对比

    Figure  9.  Comparison of different evaluation functions

    图  10  训练过程的loss变化

    Figure  10.  Loss changes during the training process

    图  11  神经元个数对评价指标的影响

    Figure  11.  Influence of the number of neurons on the evaluation metric

    图  12  时间窗口大小对评价指标的影响

    Figure  12.  Influence of the time window size on the evaluation metric

    图  13  测试集真实RUL与预测RUL的对比

    Figure  13.  Comparison of real and predicted RUL in the test set

    表  1  基学习器网络层次表

    Table  1.   Network hierarchy table of the base learner

    Network structureInput shapeOutput shape
    Conv1D(30,14)(21,8)
    Conv1D(21,8)(12,16)
    Conv1D(12,16)(10,32)
    BD-LSTM(10,32)(256)
    Dropout(256)(256)
    Output(256)(1)
    下载: 导出CSV

    表  2  各种方法结果的对比

    Table  2.   Comparison of the results of various methods

    No.MethodRMSEScore
    1MLP[17]37.5617972
    2SVR[17]20.961381
    3CNN[17]18.441286
    4RVR[17]23.801500
    5LSTM[24]16.73388
    6KNR[25]20.46729
    7RF[25]17.91479
    8BD-RNN[26]18.07N/A
    9CNN+BD-LSTM15.10344
    10Integrated CNN+BD-LSTM14.47311
    下载: 导出CSV
  • [1] Uckun S, Goebel K, Lucas P J F. Standardizing research methods for prognostics // 2008 International Conference on Prognostics and Health Management. Denver, 2008: 1
    [2] Tang D Y, Makis V, Jafari L, et al. Optimal maintenance policy and residual life estimation for a slowly degrading system subject to condition monitoring. Reliab Eng Syst Saf, 2015, 134: 198 doi: 10.1016/j.ress.2014.10.015
    [3] Canizo M, Onieva E, Conde A, et al. Real-time predictive maintenance for wind turbines using Big Data frameworks // 2017 IEEE International Conference on Prognostics and Health Management (ICPHM). Dallas, 2017: 70
    [4] Lei Y G, Li N P, Gontarz S, et al. A model-based method for remaining useful life prediction of machinery. IEEE Trans Reliab, 2016, 65(3): 1314 doi: 10.1109/TR.2016.2570568
    [5] Si X S, Wang W B, Hu C H, et al. Remaining useful life estimation – A review on the statistical data driven approaches. Eur J Oper Res, 2011, 213(1): 1 doi: 10.1016/j.ejor.2010.11.018
    [6] Liu Y C, Hu X F, Zhang W J. Remaining useful life prediction based on health index similarity. Reliab Eng Syst Saf, 2019, 185: 502 doi: 10.1016/j.ress.2019.02.002
    [7] Long Y W, Luo H W, Zhi Y, et al. Remaining useful life estimation of solder joints using an ARMA model optimized by genetic algorithm // 2018 19th International Conference on Electronic Packaging Technology (ICEPT). Shanghai, 2018: 1108
    [8] Wu W, Hu J T, Zhang J L. Prognostics of machine health condition using an improved ARIMA-based prediction method // 2007 2nd IEEE Conference on Industrial Electronics and Applications. Harbin, 2007: 1062
    [9] Zhou Y P, Huang M H. Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model. Microelectron Reliab, 2016, 65: 265 doi: 10.1016/j.microrel.2016.07.151
    [10] Tian Z G. An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring. J Intell Manuf, 2012, 23(2): 227 doi: 10.1007/s10845-009-0356-9
    [11] Mosallam A, Medjaher K, Zerhouni N. Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction. J Intell Manuf, 2016, 27(5): 1037 doi: 10.1007/s10845-014-0933-4
    [12] Khelif R, Chebel-Morello B, Malinowski S, et al. Direct remaining useful life estimation based on support vector regression. IEEE Trans Ind Electron, 2017, 64(3): 2276 doi: 10.1109/TIE.2016.2623260
    [13] Miao Q, Xie L, Cui H J, et al. Remaining useful life prediction of lithium-ion battery with unscented particle filter technique. Microelectron Reliab, 2013, 53(6): 805 doi: 10.1016/j.microrel.2012.12.004
    [14] Tobon-Mejia D A, Medjaher K, Zerhouni N, et al. A data-driven failure prognostics method based on mixture of Gaussians hidden Markov models. IEEE Trans Reliab, 2012, 61(2): 491 doi: 10.1109/TR.2012.2194177
    [15] Li Z X, Wu D Z, Hu C, et al. An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction. Reliab Eng Syst Saf, 2019, 184: 110 doi: 10.1016/j.ress.2017.12.016
    [16] Heimes F O. Recurrent neural networks for remaining useful life estimation // 2008 International Conference on Prognostics and Health Management. Denver, 2008: 1
    [17] Babu G S, Zhao P L, Li X L. Deep convolutional neural network based regression approach for estimation of remaining useful life // International Conference on Database Systems for Advanced Applications. Dallas, 2016: 214
    [18] Yuan M, Wu Y T, Lin L. Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network // 2016 IEEE International Conference on Aircraft Utility Systems (AUS). Beijing, 2016: 135
    [19] Zhang Y Z, Xiong R, He H W, et al. Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Trans Veh Technol, 2018, 67(7): 5695 doi: 10.1109/TVT.2018.2805189
    [20] Ordóñez C, Lasheras F S, Roca-Pardiñas J, et al. A hybrid ARIMA–SVM model for the study of the remaining useful life of aircraft engines. J Comput Appl Math, 2019, 346: 184 doi: 10.1016/j.cam.2018.07.008
    [21] Guo L, Li N P, Jia F, et al. A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing, 2017, 240: 98 doi: 10.1016/j.neucom.2017.02.045
    [22] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9(8): 1735 doi: 10.1162/neco.1997.9.8.1735
    [23] Wu Y T, Yuan M, Dong S P, et al. Remaining useful life estimation of engineered systems using vanilla LSTM neural networks. Neurocomputing, 2018, 275: 167 doi: 10.1016/j.neucom.2017.05.063
    [24] Zheng S, Ristovski K, Farahat A, et al. Long short-term memory network for remaining useful life estimation // 2017 IEEE International Conference on Prognostics and Health Management (ICPHM). Dallas, 2017: 88
    [25] Zhang C, Lim P, Qin A K, et al. Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE Trans Neural Networks Learn Syst, 2017, 28(10): 2306 doi: 10.1109/TNNLS.2016.2582798
    [26] Zhang J J, Wang P, Yan R Q, et al. Long short-term memory for machine remaining life prediction. J Manuf Syst, 2018, 48: 78 doi: 10.1016/j.jmsy.2018.05.011
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出版历程
  • 收稿日期:  2019-10-10
  • 刊出日期:  2020-10-25

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