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考虑不确定测量和个体差异的非线性随机退化系统剩余寿命估计

郑建飞 胡昌华 司小胜 张正新 张鑫

郑建飞, 胡昌华, 司小胜, 张正新, 张鑫. 考虑不确定测量和个体差异的非线性随机退化系统剩余寿命估计. 自动化学报, 2017, 43(2): 259-270. doi: 10.16383/j.aas.2017.c150775
引用本文: 郑建飞, 胡昌华, 司小胜, 张正新, 张鑫. 考虑不确定测量和个体差异的非线性随机退化系统剩余寿命估计. 自动化学报, 2017, 43(2): 259-270. doi: 10.16383/j.aas.2017.c150775
ZHENG Jian-Fei, HU Chang-Hua, SI Xiao-Sheng, ZHANG Zheng-Xin, ZHANG Xin. Remaining Useful Life Estimation for Nonlinear Stochastic Degrading Systems with Uncertain Measurement and Unit-to-unit Variability. ACTA AUTOMATICA SINICA, 2017, 43(2): 259-270. doi: 10.16383/j.aas.2017.c150775
Citation: ZHENG Jian-Fei, HU Chang-Hua, SI Xiao-Sheng, ZHANG Zheng-Xin, ZHANG Xin. Remaining Useful Life Estimation for Nonlinear Stochastic Degrading Systems with Uncertain Measurement and Unit-to-unit Variability. ACTA AUTOMATICA SINICA, 2017, 43(2): 259-270. doi: 10.16383/j.aas.2017.c150775

考虑不确定测量和个体差异的非线性随机退化系统剩余寿命估计

doi: 10.16383/j.aas.2017.c150775
基金项目: 

国家自然科学基金 61174030

国家自然科学基金 61573366

国家自然科学基金 61374126

国家自然科学基金 61573365

国家自然科学基金 61473094

详细信息
    作者简介:

    郑建飞火箭军工程大学控制工程系博士研究生.主要研究方向为预测与健康管理, 可靠性和预测维护.E-mail:zjf302@126.com

    司小胜火箭军工程大学控制工程系讲师.主要研究方向为预测与健康管理, 剩余寿命估计, 可靠性和预测维护.E-mail:sxs09@mails.tsinghua.edu.cn

    张正新火箭军工程大学控制工程系博士研究生.主要研究方向为预测与健康管理, 可靠性估计, 预测维护和寿命估计.E-mail:zhangzhengxin13@gmail.com

    张鑫火箭军工程大学控制工程系博士研究生.主要研究方向为故障诊断技术与寿命预测.E-mail:15691867358@163.com

    通讯作者:

    胡昌华火箭军工程大学控制工程系教授.主要研究方向为故障诊断, 可靠性工程.本文通信作者.E-mail:hch6603@263.net

Remaining Useful Life Estimation for Nonlinear Stochastic Degrading Systems with Uncertain Measurement and Unit-to-unit Variability

Funds: 

National Natural Science Foundation of China 61174030

National Natural Science Foundation of China 61573366

National Natural Science Foundation of China 61374126

National Natural Science Foundation of China 61573365

National Natural Science Foundation of China 61473094

More Information
    Author Bio:

    Ph. D. candidate in the Department of Control Engineering, Xi'an Institute of High Technology. His research interest covers prognostics and health management, reliability, and predictive maintenance

    Lecturer in the Department of Control Engineering, Xi'an Institute of High Technology. His research interest covers prognostics and health management, remaining useful life estimation, reliability and predictive maintenance

    ZHANG Zheng-Xin Ph. D. candidate in the Department of Control Engineering, Xi'an Institute of High Technology. His research interest covers prognostics and health management, reliability estimation, predictive maintenance, and lifetime estimation

    Ph. D. candidate in the Department of Control Engineering, Xi'an Institute of High Technology. His research interest covers fault diagnosis and lifetime prediction

    Corresponding author: HU Chang-Hua Professor in the Department of Control Engineering, Xi'an Institute of High Technology. His research interest covers fault diagnosis and reliability engineering. Corresponding author of this paper
  • 摘要: 剩余寿命估计是预测与健康管理的基础,是降低系统运行风险、提高系统安全性与可靠性的有效途径.针对工程实际中大量存在的非线性随机性退化系统,现有方法仅单独考虑了不确定测量或系统间个体差异对剩余寿命的影响,尚未实现同时考虑不确定测量和个体差异的剩余寿命估计.因此,本文首先建立了一种基于扩散过程的非线性退化模型,进一步通过建立的状态空间模型和Kalman滤波实现了同时考虑不确定测量和个体差异下的随机退化系统剩余寿命自适应估计,同时对漂移系数进行自适应估计,以获取非线性退化系统更加精确的剩余寿命估计.最后,将所提方法应用于疲劳裂纹和陀螺仪的监测数据,结果表明本文方法显著优于仅考虑不确定测量或仅考虑个体差异的寿命估计方法,具有潜在的工程应用价值.
    1)  本文责任编委 钟麦英
  • 图  1  A2017-T4铝合金疲劳裂纹增长轨迹

    Fig.  1  Degradation measurements of fatigue-crack growth

    图  2  3种情况下基于疲劳裂纹数据的剩余寿命估计PDF和期望值的比较结果

    Fig.  2  Comparisons of the PDFs and mean of the RULs for three cases with the fatigue crack data

    图  3  在第2.2 $\times$ 105周期监测点时3种情况下的剩余寿命PDF

    Fig.  3  PDFs for the three cases at the 2.2 $\times$ 105 cycle

    图  4  $\mu _{a, k|k}$和${\sigma _{a, k}}$基于第三组测量数据的更新

    Fig.  4  Updates of $\mu _{a, k|k}$ and ${\sigma _{a, k}}$ based on the third set of data

    图  5  基于第三组裂纹数据的剩余寿命估计的MSE比较结果

    Fig.  5  Comparisons for the MSE of the estimated remaining useful life based on the third set of data

    图  6  某型惯导系统陀螺仪漂移退化轨迹

    Fig.  6  The degradation path of the inertial navigation gyro

    图  7  基于第四组陀螺漂移数据的剩余寿命估计的MSE比较结果

    Fig.  7  Comparisons for the MSE of the estimated remaining useful life based on the fourth set of gyro data

    表  1  3种情况下对疲劳裂纹的估计结果

    Table  1  Comparisons of three degradation models with fatigue-crack growth data

    $\mu _a$ $\sigma _a$ b ${\sigma _B}$ ${\sigma _\varepsilon }$ log-LF AIC TMSE
    情况1 3.9477E-005 8.9347E-006 13.482 1.8977 - -43.1125 94.225 0.0518
    情况2 4.9223E-005 - 13.3145 0.5346 0.4907 -37.4882 82.9764 0.0259
    情况3 4.9E-003 1.9582E-004 8.1382 0.0114 0.5121 -28.9682 67.9364 0.0063
    下载: 导出CSV

    表  2  3种情况下各状态监测点疲劳裂纹的MSE值

    Table  2  MSEs of fatigue-crack growth data condition monitoring points for the three cases

    监测点($\times {10^5}$周期) 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3
    情况1 0.0067 0.0060 0.0055 0.0050 0.0053 0.0042 0.0036 0.0122 0.0034
    情况2 0.8190 0.7444 0.6603 0.5667 0.4653 0.3655 0.2640 0.1459 0.0449
    情况3 0.0076 0.0038 0.0021 0.0015 0.0015 0.0015 0.0015 0.0031 0.0032
    下载: 导出CSV

    表  3  3种情况下对陀螺仪的估计结果

    Table  3  Comparisons of three degradation models with gyros

    $\mu _a$ $\sigma _a$ b ${\sigma _B}$ ${\sigma _\varepsilon }$ log-LF AIC TMSE
    情况1 6.6895E-021 6.1544E-021 14.8843 0.0668 - 27.6830 -47.3360 66.1468
    情况2 1.4625E-013 - 13.3145 0.1563 0.0400 -2.3983 -3.2034 89.5732
    情况3 5.2151E-026 4.8076E-026 18.6422 0.0646 0.0253 27.9339 -45.8780 42.8521
    下载: 导出CSV

    表  4  3种情况下各状态监测点陀螺仪的MSE值

    Table  4  MSEs of gyros data condition monitoring points for the three cases

    监测点(h) 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0
    情况1 8.3907 11.5918 16.1759 11.4570 6.4760 4.8352 4.5324 2.4438
    情况2 9.6285 18.5416 17.4385 14.3918 10.6622 8.5542 7.1531 3.1990
    情况3 5.0858 7.9371 12.0434 8.2163 3.7278 2.2491 2.2328 1.1327
    下载: 导出CSV
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  • 收稿日期:  2015-11-23
  • 录用日期:  2016-05-23
  • 刊出日期:  2017-02-01

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