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基于最后逃逸时间的随机退化设备寿命预测方法

张建勋 杜党波 司小胜 胡昌华 郑建飞

张建勋, 杜党波, 司小胜, 胡昌华, 郑建飞. 基于最后逃逸时间的随机退化设备寿命预测方法. 自动化学报, 2022, 48(1): 249−260 doi: 10.16383/j.aas.c200260
引用本文: 张建勋, 杜党波, 司小胜, 胡昌华, 郑建飞. 基于最后逃逸时间的随机退化设备寿命预测方法. 自动化学报, 2022, 48(1): 249−260 doi: 10.16383/j.aas.c200260
Zhang Jian-Xun, Du Dang-Bo, Si Xiao-Sheng, Hu Chang-Hua, Zheng Jian-Fei. Lifetime prediction for stochastic deteriorating systems based on the last exit time. Acta Automatica Sinica, 2022, 48(1): 249−260 doi: 10.16383/j.aas.c200260
Citation: Zhang Jian-Xun, Du Dang-Bo, Si Xiao-Sheng, Hu Chang-Hua, Zheng Jian-Fei. Lifetime prediction for stochastic deteriorating systems based on the last exit time. Acta Automatica Sinica, 2022, 48(1): 249−260 doi: 10.16383/j.aas.c200260

基于最后逃逸时间的随机退化设备寿命预测方法

doi: 10.16383/j.aas.c200260
基金项目: 国家自然科学基金(61903376, 61833016, 61922089, 61773386, 61673311, 61703244), 陕西省自然科学基金(2020JQ-489)资助
详细信息
    作者简介:

    张建勋:火箭军工程大学讲师. 主要研究方向为预测与健康管理, 退化过程建模和剩余寿命估计. 本文通信作者. E-mail: zhang200735@163.com

    杜党波:火箭军工程大学讲师. 主要研究方向为预测与健康管理, 剩余寿命估计. E-mail: ddb_effort@126.com

    司小胜:火箭军工程大学教授. 主要研究方向为预测与健康管理, 剩余寿命估计和可靠性与预测维护. E-mail: sxs09@mails.tsinghua.edu.cn

    胡昌华:火箭军工程大学教授. 主要研究方向为故障诊断, 可靠性工程和预测与健康管理. E-mail: hch66603@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, 剩余寿命估计. E-mail: zjf302@126.com

Lifetime Prediction for Stochastic Deteriorating Systems Based on the Last Exit Time

Funds: Supported by National Natural Science Foundation of China (61903376, 61833016, 61922089, 61773386, 61673311, 61703244), and Natural Science Foundation of Shaanxi Province (2020JQ-489)
More Information
    Author Bio:

    ZHANG Jian-Xun Lecturer at Rocket Force University of Engineering. His research interest covers prognostics and health management, degradation process modeling, and remaining useful life estimation. Corresponding author of this paper

    DU Dang-Bo Lecturer at Rocket Force University of Engineering. His research interest covers prognostics and health management, and remaining useful life estimation

    SI Xiao-Sheng Professor at Rocket Force University of Engineering. His research interest covers prognostics and health management, remaining useful life estimation, and reliability and predictive maintenance

    HU Chang-Hua Professor at Rocket Force University of Engineering. His research interest covers fault diagnosis, reliability engineering, and prognostics and health management

    ZHENG Jian-Fei Associate professor at Rocket Force University of Engineering. His research interest covers prognostics and health management, and remaining useful life estimation

  • 摘要: 现有基于随机退化过程建模的寿命预测研究中, 通常用退化过程的首达时间(First passage time, FPT)来定义寿命. 但是, 这种寿命定义较为保守, 可能会导致其明显小于设备实际寿命. 鉴于此, 基于最后逃逸时间(Last exit time, LET)的概念, 给出一种新的寿命与剩余寿命(Remaining useful life, RUL)定义方式. 在该新框架下, 提出一种基于最后逃逸时间的寿命预测方法, 推导得到最后逃逸时间下基于Wiener退化过程模型的寿命与剩余寿命表达形式, 讨论了该方法与传统首达时间下寿命预测方法之间的关系. 此外, 通过数值仿真验证了该方法的正确性, 并对模型参数进行了敏感性分析. 最后, 通过轴承以及激光器的实际退化数据说明了该方法的有效性、可行性以及潜在的工程应用价值.
  • 图  1  随机过程中首达时间与最后逃逸时间

    Fig.  1  The first passage time and last exit time of the stochastic process

    图  2  轴承RMS退化轨迹

    Fig.  2  The RMS degradation paths of bearing

    图  3  寿命分布PDF

    Fig.  3  PDF of the lifetime distribution

    图  4  不同Tmax取值下寿命分布PDF

    Fig.  4  PDF of the lifetime distribution with different Tmax

    图  5  4种Tmax取值下寿命分布PDF

    Fig.  5  PDF of the lifetime distribution with four different Tmax

    图  6  随机效应影响下寿命分布PDF

    Fig.  6  PDF of the lifetime distribution with random effects

    图  7  不同μ取值下寿命分布PDF

    Fig.  7  PDF of the lifetime distribution with different μ

    图  8  不同${\sigma _B}$取值下寿命分布PDF

    Fig.  8  PDF of the lifetime distribution with different ${\sigma _B}$

    图  9  轴承退化轨迹

    Fig.  9  Degradation path of a bearing

    图  10  不同测试时间处剩余寿命分布PDF

    Fig.  10  PDF of the RUL distribution at different testing time

    图  11  不同测试时间处剩余寿命期望

    Fig.  11  Means of the RUL at different testing time

    图  12  激光器的退化轨迹

    Fig.  12  Degradation paths of the laser device

    图  13  第8组激光器退化轨迹

    Fig.  13  Degradation path of the 8th laser device

    图  14  不同测试时间处剩余寿命分布PDF

    Fig.  14  PDF of the RUL distribution at different testing times

    图  15  T0概率密度函数

    Fig.  15  Probability density function of T0

    表  1  轴承真实寿命对比(min)

    Table  1  Comparison of bearings' actual lifetime (min)

    轴承数据实际寿命首达时间下寿命最后逃逸时间下寿命
    1_112391110
    1_216174110
    1_3159149149
    1_5524749
    2_1491488488
    2_2161144161
    2_3533478533
    2_4423838
    2_5399199284
    3_1253825242529
    3_3371352362
    3_4151514561461
    3_51147498
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-04-28
  • 录用日期:  2020-08-05
  • 修回日期:  2020-07-07
  • 网络出版日期:  2021-11-23
  • 刊出日期:  2022-01-25

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