Document Type

Article

Publication Date

5-1-2013

Abstract

Repeated measures degradation studies are used to assess product or component reliability when there are few or even no failures expected during a study. Such studies are often used to assess the shelf life of materials, components, and products. We show how to evaluate the properties of proposed test plans. Such evaluations are needed to identify statistically efficient tests. We consider test plans for applications where parameters related to the degradation distribution or the related lifetime distribution are to be estimated. We use the approximate large-sample variance-covariance matrix of the parameters of a mixed effects linear regression model for repeated measures degradation data to assess the effect of sample size (number of units and number of measurements within the units) on estimation precision of both degradation and failure-time distribution quantiles. We also illustrate the complementary use of simulation-based methods for evaluating and comparing test plans. These test-planning methods are illustrated with two examples. We provide the R code and examples as supplementary materials (available online on the journal web site) for this article. © 2013 Copyright Taylor and Francis Group, LLC.

Publication Source (Journal or Book title)

Technometrics

First Page

122

Last Page

134

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