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基于流模型的缺失数据生成方法在剩余寿命预测中的应用

张博玮 郑建飞 胡昌华 裴洪 董青

张博玮, 郑建飞, 胡昌华, 裴洪, 董青. 基于流模型的缺失数据生成方法在剩余寿命预测中的应用. 自动化学报, 2022, 45(x): 1−12 doi: 10.16383/j.aas.c220219
引用本文: 张博玮, 郑建飞, 胡昌华, 裴洪, 董青. 基于流模型的缺失数据生成方法在剩余寿命预测中的应用. 自动化学报, 2022, 45(x): 1−12 doi: 10.16383/j.aas.c220219
Zhang Bo-Wei, Zheng Jian-Fei, Hu Chang-Hua, Pei Hong, Dong Qing. Missing data generation method based on flow model and its application in remaining life prediction. Acta Automatica Sinica, 2022, 45(x): 1−12 doi: 10.16383/j.aas.c220219
Citation: Zhang Bo-Wei, Zheng Jian-Fei, Hu Chang-Hua, Pei Hong, Dong Qing. Missing data generation method based on flow model and its application in remaining life prediction. Acta Automatica Sinica, 2022, 45(x): 1−12 doi: 10.16383/j.aas.c220219

基于流模型的缺失数据生成方法在剩余寿命预测中的应用

doi: 10.16383/j.aas.c220219
基金项目: 国家自然科学基金(61773386, 61833016, 61922089, 62073336, 62103433) 资助, 陕西省自然科学基金(2020JM-360) 资助
详细信息
    作者简介:

    张博玮:火箭军工程大学导弹工程学院硕士研究生. 主要研究方向为预测与健康管理, 预测维护和深度神经网络. E-mail: zbw204@126.com

    郑建飞:火箭军工程大学导弹工程学院副教授. 主要研究方向为预测与健康管理, 可靠性和预测维护.本文通信作者. E-mail: zjf302@126.com

    胡昌华:火箭军工程大学导弹工程学院教授. 主要研究方向包括故障诊断和预测, 寿命预测和容错控制. E-mail: hch666@163.com

    裴洪:火箭军工程大学导弹工程学院讲师. 主要研究方向为预测与健康管理, 剩余寿命智能预测. E-mail: ph2010hph@sina.com

    董青:火箭军工程大学导弹工程学院博士研究生. 主要研究方向为预测与健康管理, 预测维护. E-mail: 18756528162@163.com

Missing Data Generation Method Based on Flow Model and Its Application in Remaining Life Prediction

Funds: Supported by National Natural Science Foundation of China(61773386, 61833016, 61922089, 62073336, 62103433), Natural Science Foundation of Shaanxi Province(2020JM-360)
More Information
    Author Bio:

    ZHANG Bo-Wei Master student of the School of Missile Engineering, Rocket Force Engineering University. His research interest covers prognostics and health management, predictive maintenance and deep neural networks

    ZHENG Jian-Fei Associate Professor of the School of Missile Engineering, Rocket Force Engineering University. His research interest covers prognostics and health management, reliability, and predictive maintenance. Corresponding author of this paper

    HU Chang-Hua Professor of the School of Missile Engineering, Rocket Force Engineering University. His research interest covers fault diagnosis and prediction, life prognosis, and fault tolerant control

    PEI Hong Lecturer of the School of Missile Engineering, Rocket Force Engineering University. His research interest covers prognostics and health management, remaining useful life intelligent prediction

    DONG Qing Ph.D. candidate of the School of Missile Engineering, Rocket Force Engineering University. His research interest covers prognostics and health management, predictive maintenance

  • 摘要: 针对缺失数据生成模型精度低和训练速度慢的问题, 本文基于流模型框架提出了一种改进非线性独立成分估计(Nonlinear independent components estimation, NICE)的缺失时间序列生成方法. 该方法依靠流模型框架生成模型精度高、训练过程速度快的优势, 并结合粒子群优化算法(Particle swarm optimization, PSO) 优化NICE生成网络采样的退火参数, 训练学习监测数据的真实分布, 从而实现对数据缺失部分的最优填补. 为进一步拓宽所提方法的应用范围, 利用基于流模型的缺失数据生成方法得到的生成数据, 通过建立融合注意力机制的双向长短时记忆网络(Bidirectional long short-term memory with attention, Bi-LSTM-Att)的退化设备预测模型, 实现设备剩余寿命的准确预测. 最后, 通过锂电池退化数据的实例研究, 验证了该方法的有效性和潜在应用价值.
  • 图  1  PSO-NICE网络框架图

    Fig.  1  PSO-NICE network frame diagram

    图  2  Bi-LSTM-Att网络框架图

    Fig.  2  Bi-LSTM-Att network frame diagram

    图  3  缺失数据生成和RUL预测流程图

    Fig.  3  Missing data generation and RUL prediction flowchart

    图  4  CS2电池组容量退化轨迹

    Fig.  4  CS2 battery pack capacity degradation trajectory

    图  5  70%缺失率下不同方法的生成效果

    Fig.  5  The generation effect of different methods under 70% missing rate

    图  6  不同缺失率下PSO迭代优化过程

    Fig.  6  Iterative optimization process of PSO under different missing rates

    图  7  不同缺失率下的填补效果

    Fig.  7  Generation effect under different missing rate

    图  8  0%缺失率下现有常用方法的预测效果

    Fig.  8  Prediction effect of existing common methods under 0% missing rate

    图  9  不同缺失率填补后Bi-LSTM-Att的预测效果

    Fig.  9  The prediction effect of Bi-LSTM-Att after filling with different missing rates

    图  10  四种缺失率填补后Bi-LSTM-Att重复预测效果

    Fig.  10  Bi-LSTM-Att repeated prediction effect after four missing rates imputation

    表  1  CS2_37构造的原始数据和不同缺失率下的训练数据

    Table  1  Original data constructed by CS2_37 and training data with different missing rates

    数据类型 缺失率 样本数量
    原始数据 0 850
    缺失数据 10% 771
    30% 643
    50% 513
    70% 438
    下载: 导出CSV

    表  2  CS2数据集构造RUL预测的训练样本和验证样本

    Table  2  CS2 dataset constructs training samples and validation samples for RUL prediction

    样本类型 样本组成 样本量
    训练样本 CS2_35 851
    CS2_36 944
    CS2_38 991
    CS2_37 (0$\sim$600) 600
    验证样本 CS2_37 (601$\sim$1006) 406
    下载: 导出CSV

    表  3  不同缺失率下的PSO-NICE模型参数

    Table  3  PSO-NICE model parameters with different missing rates

    训练数据缺失率 样本剩余量 分块加性耦合层数 耦合层数 每层神经元 批处理量 NICE迭代次数 粒子维度大小 粒子群大小 PSO迭代次数
    10% 771 16 5 1000 128 1000 2 2 15
    30% 643 14 5 1000 64 1000 2 2 15
    50% 513 12 5 1000 64 1000 2 2 15
    70% 438 10 5 1000 64 1000 2 2 15
    下载: 导出CSV

    表  4  不同缺失率下各方法生成样本与完整样本的EM距离

    Table  4  The EM distance between generation sample and the complete sample under different missing rates

    缺失率 不处理 VAE GAN NICE PSO-NICE DCGAN-KS[11]
    10% 0.081 0.064 0.056 0.012 0.007 0.024
    30% 0.218 0.175 0.159 0.014 0.011 0.049
    50% 0.357 0.282 0.207 0.024 0.013 0.072
    70% 0.431 0.397 0.284 0.028 0.015 0.122
    下载: 导出CSV

    表  5  现有常用预测网络的参数

    Table  5  Parameters of existing common prediction networks

    预测网络 网络层数 每层单元数 Attention单元数 全连接层数 每层神经元个数 随机种子 批处理量 迭代次数
    RNN 1 64 0 1 1 3 16 4
    GRU 1 64 0 1 1 0 16 5
    LSTM 1 64 0 1 1 0 16 5
    Bi-LSTM 2 64 0 1 1 0 16 5
    Bi-LSTM-Att 2 64 1 1 1 0 16 5
    下载: 导出CSV

    表  6  不同预测方法的效果评估

    Table  6  Effectiveness evaluation of different forecasting methods

    预测方法 缺失率 RMSE $R^2$ 运行时间/秒
    RNN 0% 0.1679 –0.77041 14
    GRU 0.1376 0.72191 15
    LSTM 0.0902 0.8713 15
    Bi-LSTM 0.0746 0.9114 17
    Bi-LSTM-Att 0% 0.0213 0.9907 26
    10% 0.022 0.99
    30% 0.0224 0.9898
    50% 0.0328 0.978
    70% 0.0424 0.9632
    下载: 导出CSV

    表  7  不同缺失率填补后Bi-LSTM-Att重复预测量化结果

    Table  7  Quantification results of Bi-LSTM-Att repeated prediction after imputation with different missing rates

    缺失率 预测均值 95%置信区间
    0% 0.0213 [0.037 9, 0.046 8]
    10% 0.0215 [0.046 0, 0.049 3]
    30% 0.0294 [0.052 6, 0.065 7]
    50% 0.0300 [0.042 8, 0.102 4]
    70% 0.0468 [0.056 0, 0.105 4]
    下载: 导出CSV
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  • 收稿日期:  2022-03-23
  • 录用日期:  2022-08-22
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