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双时间尺度下的设备随机退化建模与剩余寿命预测方法

张正新 胡昌华 司小胜 张伟

张正新, 胡昌华, 司小胜, 张伟. 双时间尺度下的设备随机退化建模与剩余寿命预测方法. 自动化学报, 2017, 43(10): 1789-1798. doi: 10.16383/j.aas.2017.c160509
引用本文: 张正新, 胡昌华, 司小胜, 张伟. 双时间尺度下的设备随机退化建模与剩余寿命预测方法. 自动化学报, 2017, 43(10): 1789-1798. doi: 10.16383/j.aas.2017.c160509
ZHANG Zheng-Xin, HU Chang-Hua, SI Xiao-Sheng, ZHANG Wei. Degradation Modeling and Remaining Useful Life Prediction with Bivariate Time Scale. ACTA AUTOMATICA SINICA, 2017, 43(10): 1789-1798. doi: 10.16383/j.aas.2017.c160509
Citation: ZHANG Zheng-Xin, HU Chang-Hua, SI Xiao-Sheng, ZHANG Wei. Degradation Modeling and Remaining Useful Life Prediction with Bivariate Time Scale. ACTA AUTOMATICA SINICA, 2017, 43(10): 1789-1798. doi: 10.16383/j.aas.2017.c160509

双时间尺度下的设备随机退化建模与剩余寿命预测方法

doi: 10.16383/j.aas.2017.c160509
基金项目: 

国家自然科学基金 61473094

国家自然科学基金 61374126

国家自然科学基金 61573366

中国科协青年人才托举工程 2016QNRC001

国家自然科学基金 61573365

国家自然科学基金 61773386

陕西省自然科学基金 2015JQ6235

详细信息
    作者简介:

    张正新 火箭军工程大学博士研究生.主要研究方向为预测与健康管理, 吋靠性估计, 预测维护和寿命估计.E-mail:zhangzhengxinl3@gmail.com

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

    张伟 火箭军工程大学信息工程系副教授.主要研究方向为模式识别和通信技术.E-mail:zhangl966wei@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:hch66603@163.com

Degradation Modeling and Remaining Useful Life Prediction with Bivariate Time Scale

Funds: 

National Natural Science Foundation of China 61473094

National Natural Science Foundation of China 61374126

National Natural Science Foundation of China 61573366

Young Elite Scientists Sponsorship Program of China Association for Science and Technology 2016QNRC001

National Natural Science Foundation of China 61573365

National Natural Science Foundation of China 61773386

Nature Science Foundation of Shaanxi Province 2015JQ6235

More Information
    Author Bio:

    Ph. D. candidate in the Department of Automation Technology, Xi′an Institute of High Technology. His research interest covers prognostics and health management, reliability estimation, predictive maintenance, and lifetime estimation

    Ph. D. at Xi′an Institute of High Technology. His research interest covers prognostics and health management, remaining useful life estimation, reliability and predictive maintenance

    Associate professor in the Department of Information Technology, Xi′an Institute of High Technology. Her research interest covers pattern recognition and communication technology

    Corresponding author: HU Chang-Hua  Professor in the Department of Automation Technology, Xi′an Institute of High Technology. His research interest covers fault diagnosis and reliability engineering. Corresponding author of this paper.E-mail:hch66603@163.com
  • 摘要: 基于退化建模的剩余寿命预测(Remaining useful life,RUL)是当前可靠性领域研究的热点.现有的退化模型都是针对单个时间尺度下的退化设备,缺少对设备性能变化与多个时间尺度相关的退化建模与剩余寿命预测方法.鉴于此,本文基于Wiener过程提出了一种双时间尺度随机退化建模与剩余寿命预测方法,用随机比例系数描述不同时间尺度之间的不确定关系,推导出丫首达时间意义下设备的双时间尺度剩余寿命分布,讨论了其与基于单时间尺度退化模型得到的剩余寿命分布之间的关系,并给出了基于历史退化数据的未知参数极大似然估计方法.最后,将所提方法应用到惯性平台关键器件陀螺仪的退化建模与剩余寿命预测中,验证了方法的有效性.
    1)  本文责任编委 姜斌
  • 图  1  不同时间尺度下陀螺仪的漂移系数

    Fig.  1  Gyroscopic drifts under different time scales

    图  2  双时间尺度下陀螺仪寿命的PDF

    Fig.  2  PDF of gyroscope #3 under bivariate time scale

    图  3  时间尺度$t$下陀螺仪RUL的PDF

    Fig.  3  PDF of RUL under time scale $t$

    图  4  储存时间尺度下$M_1$和$M_2$下剩余寿命预测的MSE

    Fig.  4  MSE comparison of $M_1$ and $M_2$ under time scale $t$

    图  5  时间尺度$\tau$下陀螺仪RUL的PDF

    Fig.  5  PDF of RUL under time scale $\tau$

    图  6  检测时间尺度下$M_1$和$M_2$下剩余寿命预测的MSE

    Fig.  6  MSE comparison of $M_1$ and $M_2$ under time scale $\tau$

    表  1  #3陀螺仪的模型参数估计结果

    Table  1  Estimated parameters based on degradation path #3

    时间尺度 $\lambda_0$ $\lambda_1$ $\lambda_2$ $\sigma_B$ $\sigma_W$ $\ell(\hat{\boldsymbol{\varphi}}|\boldsymbol{x})$ AIC
    $t$ -2.8959E-3 3.0662E-3 - 1.0845E-2 - 18.8689 -31.7378
    $\tau$ -3.5932E-3 - 1.1095E-2 - 1.1251E-2 23.4324 -40.8648
    $[t, \tau]$ 3.6148E-3 9.5126E-5 1.1440E-2 1.8146E-5 5.5160E-3 24.9530 -39.9060
    下载: 导出CSV
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  • 收稿日期:  2016-07-08
  • 录用日期:  2016-11-03
  • 刊出日期:  2017-10-20

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