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基于两阶段自适应Wiener过程的剩余寿命预测方法

董青 郑建飞 胡昌华 李冰 牟含笑

董青, 郑建飞, 胡昌华, 李冰, 牟含笑. 基于两阶段自适应Wiener过程的剩余寿命预测方法. 自动化学报, 2021, 47(x): 1−15 doi: 10.16383/j.aas.c210057
引用本文: 董青, 郑建飞, 胡昌华, 李冰, 牟含笑. 基于两阶段自适应Wiener过程的剩余寿命预测方法. 自动化学报, 2021, 47(x): 1−15 doi: 10.16383/j.aas.c210057
Dong Qing, Zheng Jian-Fei, Hu Chang-Hua, Li Bing, Mu Han-Xiao. Remaining useful life prognostic method based on two-stage adaptive wiener process. Acta Automatica Sinica, 2021, 47(x): 1−15 doi: 10.16383/j.aas.c210057
Citation: Dong Qing, Zheng Jian-Fei, Hu Chang-Hua, Li Bing, Mu Han-Xiao. Remaining useful life prognostic method based on two-stage adaptive wiener process. Acta Automatica Sinica, 2021, 47(x): 1−15 doi: 10.16383/j.aas.c210057

基于两阶段自适应Wiener过程的剩余寿命预测方法

doi: 10.16383/j.aas.c210057
基金项目: 国家自然科学基金(61773386, 61833016, 61922089, 62073336)资助, 陕西省自然科学基金(2020JM-360)资助
详细信息
    作者简介:

    董青:火箭军工程大学硕士. 主要研究方向为预测与健康管理, 预测维护. E-mail: 18756528162@163.com

    郑建飞:火箭军工程大学副教授. 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:火箭军工程大学教授, 长江学者. 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

    李冰:火箭军装备部驻西安地区第一军事代表室工程师. 主要研究方向为可靠性和预测维护. E-mail: yanyunsheng-518@163.com

    牟含笑:火箭军工程大学硕士. 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: 18730269356@163.com

Remaining Useful Life Prognostic Method Based on Two-stage Adaptive Wiener Process

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

    DONG Qing Master of the Rocket Force University of Engineering. His research interests include prognostics and health management, and predictive maintenance

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

    HU Chang-Hua Professor at the Rocket Force University of Engineering. His research interests include fault diagnosis and prediction, life prognosis, and fault tolerant control

    LI Bing The First Military Representative Office of the Rocket Force Equipment Department in Xi'an. His research interest covers reliability, and predictive maintenance

    Mu Han-Xiao Master of the Rocket Force University of Engineering. Her research interests include prognostics and health management, predictive maintenance and deep neural networks

  • 摘要: 针对退化过程呈现两阶段特征的一类随机退化设备, 现有剩余寿命预测方法不适用于测量间隔分布不均匀、监测数据的测量频率与历史数据频率不一致的情况, 并且忽略了自适应漂移的可变性. 鉴于此, 提出了一种新的考虑个体差异性的两阶段自适应Wiener过程剩余寿命预测模型与方法. 首先, 基于自适应Wiener过程分阶段构建随机退化模型, 在首达时间意义下推导出寿命和剩余寿命解析式. 然后, 结合Kalman滤波技术和期望最大化算法进行参数自适应更新, 同时利用赤池信息准则实现退化模型变点的辨识. 最后, 通过蒙特卡洛仿真和锂电池实例, 验证了本文所提方法的有效性和实用价值.
  • 图  1  两种模型RUL预测结果

    Fig.  1  RUL prediction results of the two models

    图  2  两种模型RUL预测绝对误差

    Fig.  2  Absolute error of RUL prediction of the two models

    图  3  锂电池容量退化

    Fig.  3  Lithium battery capacity degradation

    图  4  CS2-37锂电池SIC值

    Fig.  4  SIC value of CS2-37 lithium battery

    图  5  第一阶段模型参数更新

    Fig.  5  The first stage of model parameter updating

    图  6  第二阶段模型参数更新

    Fig.  6  The second stage of model parameter updating

    图  7  CS2-37模型拟合效果

    Fig.  7  Fitting effect of CS2-37 model

    图  8  CS2-37的RUL预测结果

    Fig.  8  RUL prediction results of CS2-37

    图  9  RUL预测绝对误差

    Fig.  9  Absolute error of RUL prediction

    图  10  $\alpha {\rm{ - }}\beta $性能图

    Fig.  10  $\alpha {\rm{ - }}\beta $ performance chart

    表  1  不同监测点相对误差的比较结果

    Table  1  Comparison results of relative error at different monitoring points

    方法不同监测点相对误差
    20406080
    提出模型20.8%19.6%15.2%12.3%
    Zhang模型27.2%25.3%20.1%17.6%
    下载: 导出CSV

    表  2  不同寿命分位点相对误差的比较结果

    Table  2  Comparison results of relative error at different life quantiles

    方法寿命分位点相对误差
    35%55%75%95%
    提出模型6.04%7.65%7.91%0.66%
    Zhang模型11.4%9.37%9.68%1.46%
    下载: 导出CSV
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  • 收稿日期:  2021-01-20
  • 修回日期:  2021-03-29
  • 网络出版日期:  2021-06-14

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