Time series modeling of degradation due to outdoor weathering

被引:19
作者
Chan, Victor [1 ]
Meeker, William Q. [2 ]
机构
[1] Western Washington Univ, Dept Math, Bellingham, WA 98225 USA
[2] Iowa State Univ, Dept Stat, Ames, IA USA
关键词
confidence interval; photodegradation; reliability; service life prediction;
D O I
10.1080/03610920701653169
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Environmental variables have an important effect on the reliability of many products such as coatings and polymeric composites. Long-term prediction of the performance or service life of such products must take into account the probabilistic/stochastic nature of the outdoor weather. In this article, we propose a time series modeling procedure to model the time series data of daily accumulated degradation. Daily accumulated degradation is the total amount of degradation accrued within one day and can be obtained by using a degradation rate model for the product and the weather data. The fitted model of the time series can then be used to estimate the future distribution of cumulative degradation over a period of time, and to compute reliability measures such as the probability of failure. The modeling technique and estimation method are illustrated using the degradation of a solar reflector material. We also provide a method to construct approximate confidence intervals for the probability of failure.
引用
收藏
页码:408 / 424
页数:17
相关论文
共 15 条
  • [1] BAUER DR, 2002, SERVICE LIFE PREDICT
  • [2] BAUER DR, 1999, SERVICE LIFE PREDICT
  • [3] Brockwell PJ., 1996, INTRO TIME SERIES FO
  • [4] Block bootstrap estimation of the distribution of cumulative outdoor degradation
    Chan, V
    Lahiri, SN
    Meeker, WQ
    [J]. TECHNOMETRICS, 2004, 46 (02) : 215 - 224
  • [5] Gill P. E., 1981, PRACTICAL OPTIMIZATI
  • [6] JORGENSEN G, 2002, SERVICE LIFE PREDICT
  • [7] JORGENSEN GJ, 1996, ASTM STP, V1294
  • [8] Lawson CL., 1974, Solving Least Squares Problems
  • [9] MARTIN JW, 2002, SERVICE LIFE PREDICT
  • [10] Martin JW, 1996, METHODOLOGIES PREDIC