Data Management and a Spectral Solar UV Network

Kaetzel, Lawrence J

doi:10.1021/bk-2002-0805.ch005

Cited by 2 publications

(1 citation statement)

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“…For weathering applications, in the past decades, many solar UV monitoring sites have been established at different geographical locations within the United States and worldwide (e.g., Kaetzel 2001). The solar UV spectrum and intensity, as well as temperature and relative humidity are recorded at high resolution.…”

Section: Concluding Remarks and Areas For Future Researchmentioning

confidence: 99%

Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data

Hong¹,

Duan²,

Meeker³

et al. 2015

Technometrics

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Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables, such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this article, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape-restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity). This article has supplementary material online.Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables, such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this article, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape-restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity). This article has supplementary material online.

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Section: Concluding Remarks and Areas For Future Researchmentioning

confidence: 99%

Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data

Hong¹,

Duan²,

Meeker³

et al. 2015

Technometrics

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A Statistical Model for Linking Field and Laboratory Exposure Results for a Model Coating

Vaca-Trigo

Meeker

Service Life Prediction of Polymeric Materials

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Today's manufacturers need accelerated test (AT) methods that can usefully predict service life in a timely manner. For example, automobile manufacturers would like to develop a three-month test to predict 10-year field reliability of a coating system (an acceleration factor of 40). Developing a methodology to simulate outdoor weathering is a particularly challenging task and most previous attempts to establish an adequate correlation between laboratory tests and field experience has met with failure. Difficulties arise, for example, because the intensity and the frequency spectrum of ultraviolet (UV) radiation from the Sun are highly variable, both temporally and spatially and because there is often little understanding of how environmental variables affect chemical degradation processes. This paper describes the statistical aspects of a cooperative project being conducted at the U.S. National Institute of Standards and Technology (NIST) to generate necessary experimental data and the development of a model relating cumulative damage to environmental variables like UV spectrum and intensity, as well as temperature and relative humidity. The parameters of the cumulative damage are estimated from the laboratory data. The adequacy of the model predictions are assessed by comparing with specimens tested in an outdoor environment for which the environmental variables were carefully measured.

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Data Management and a Spectral Solar UV Network

Cited by 2 publications

References 0 publications

Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data

Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data

A Statistical Model for Linking Field and Laboratory Exposure Results for a Model Coating

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