引用本文:郑龙,杜永浩,邢立宁,等.基于退化数据与marker数据综合的产品可靠性建模分析[J].控制与决策,2020,35(2):461-468
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基于退化数据与marker数据综合的产品可靠性建模分析
郑龙1, 杜永浩2, 邢立宁2, 彭宝华2, 周忠宝1, 文龙3
(1. 湖南大学工商管理学院,长沙410082;2. 国防科技大学系统工程学院,长沙410073;3. 西安卫星测控中心地面网管理中心,西安710043)
摘要:
基于性能退化数据的可靠性建模分析方法,为现代工业中长寿命、高可靠产品的可靠性研究提供了重要途径,但在破坏性测量中无法获得足够的产品性能退化数据.对此,综合利用产品性能退化数据和marker数据对产品可靠性进行建模,不仅能够提高可靠性评估的精度,还可以在产品运行过程中通过对marker的测量来预测产品剩余寿命,从而为产品维修、更换以及备件决策提供依据.采用二元Wiener过程对产品的性能参数和marker进行建模,给出模型参数的估计方法和基于marker测量数据的剩余寿命预测方法,并通过仿真示例验证所提出方法的有效性.
关键词:  可靠性建模  性能退化  marker  二元Wiener过程  寿命预测  参数估计
DOI:10.13195/j.kzyjc.2018.0595
分类号:TB114.3
基金项目:国家863计划项目(2011AA7074112);国家自然科学基金项目(61773120, 71331008, U1501254);高等学校全国优秀博士学位论文作者专项资金项目(2014-92);湖南省教育科学“十三五”规划项目(XJK016 BGD009);湖南省教改项目(20150001);广东省科技计划项目(2015B010131015, 2015B010108006);广东 省高等学校国际暨港澳台科技合作创新平台项目(2015KGJHZ023).
Product reliability modeling and analysis using degradation and marker data
ZHENG Long1,DU Yong-hao2,XING Li-ning2,PENG Bao-hua2,ZHOU Zhong-bao1,WEN Long3
(1. Business School,Hunan University,Changsha410082,China;2. College of Systems Engineering,National University of Defense Technology,Changsha410073,China;3. Ground Station Network Management Center,Xián Satellite Monitoring and Control Center,Xián710043,China)
Abstract:
The method of reliability modeling and analysis based on performance degradation data provides an important way for the reliability research of long life and high reliability products in modern industry, but the degradation data cannot be obtained adequately during a destructive inspection. To solve this problem, this paper proposes to use both degradation data and marker data to make reliability inference, which can improve the precision of reliability inference, and make residual lifetime distribution prediction available through inspection of marker during working, contributing to the decision making in maintenance, replacement and spare parts. The performance degradation and marker are modeled with a bivariate Wiener process and, the methods for parameter estimation and residual lifetime distribution prediction are provided. Finally, a simulated example is presented to show the effectiveness of the proposed method.
Key words:  reliability modeling  degradation  marker  bivariate Wiener process  lifetime prediction  parameter estimation

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