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High reliability systems generally require individual system components having extremely high reliability over long periods of time. Short product development times require reliability tests to be conducted with severe time constraints. Frequently few or no failures occur during such tests, even with acceleration. Thus, it is difficult to assess reliability with traditional life tests that record only failure times. For some components, degradation measures can be taken over time. A relationship between component failure and amount of degradation makes it possible to use degradation models and data to make inferences and predictions about a failure-time distribution. This article describes degradation reliability models that correspond to physicalfailure mechanisms. We explain the connection between degradation reliability models and failure-time reliability models. Acceleration is modeled by having an acceleration model that describes the effect that temperature (or another accelerating variable) has on the rate of a failure-causing chemical reaction. Approximate maximum likelihood estimation is used to estimate model parameters from the underlying mixed-effects nonlinear regression model. Simulation-based methods are used to compute confidence intervals for quantities of interest (e. g., failure probabilities). Finally we use a numerical example to compare the results of accelerated degradation analysis and traditional accelerated life-test failure-time analysis. Statistics and Probability CommentsThis preprint has been published in Technometrics 40 (1998): 89-99. AbstractHigh reliability systems generally require individual system components having extremely high reliability o ver long periods of time. Short product development times require reliability tests to be conducted with severe time constraints. Frequently few or no failures occur during such tests, even with acceleration. Thus, it is di cult to assess reliability w i t h traditional life tests that record only failure times. For some components, degradation measures can be taken over time. A relationship between component failure and amount o f degradation makes it possible to use degradation models and data to make inferences and predictions about a failure-time distribution.This paper describes degradation reliability models that correspond to physical-failure mechanisms. We explain the connection between degradation reliability models and failuretime reliability m o d e l s . Acceleration is modeled by h a ving an acceleration model that describes the e ect that temperature (or another accelerating variable) has on the rate of a failure-causing chemical reaction. Approximate maximum likelihood estimation is used to estimate model parameters from the underlying mixed-e ects nonlinear regression model. Simulation-based methods are used to compute con dence intervals for quantities of interest (e.g., failure probabilities). Finally we u s e a n umerical example to compare the results of accelerated degradation analysis and traditional accelerated lif...

High reliability systems generally require individual system components having extremely high reliability over long periods of time. Short product development times require reliability tests to be conducted with severe time constraints. Frequently few or no failures occur during such tests, even with acceleration. Thus, it is difficult to assess reliability with traditional life tests that record only failure times. For some components, degradation measures can be taken over time. A relationship between component failure and amount of degradation makes it possible to use degradation models and data to make inferences and predictions about a failure-time distribution. This article describes degradation reliability models that correspond to physicalfailure mechanisms. We explain the connection between degradation reliability models and failure-time reliability models. Acceleration is modeled by having an acceleration model that describes the effect that temperature (or another accelerating variable) has on the rate of a failure-causing chemical reaction.Approximate maximum likelihood estimation is used to estimate model parameters from the underlying mixed-effects nonlinear regression model. Simulation-based methods are used to compute confidence intervals for quantities of interest (e. g., failure probabilities Abstract High reliability systems generally require individual system components having extremely high reliability o ver long periods of time. Short product development times require reliability tests to be conducted with severe time constraints. Frequently few or no failures occur during such tests, even with acceleration. Thus, it is di cult to assess reliability w i t h traditional life tests that record only failure times. For some components, degradation measures can be taken over time. A relationship between component failure and amount o f degradation makes it possible to use degradation models and data to make inferences and predictions about a failure-time distribution. This paper describes degradation reliability models that correspond to physical-failure mechanisms. We explain the connection between degradation reliability models and failuretime reliability m o d e l s . Acceleration is modeled by h a ving an acceleration model that describes the e ect that temperature (or another accelerating variable) has on the rate of a failure-causing chemical reaction. Approximate maximum likelihood estimation is used to estimate model parameters from the underlying mixed-e ects nonlinear regression model. Simulation-based methods are used to compute con dence intervals for quantities of interest (e.g., failure probabilities). Finally we u s e a n umerical example to compare the results of accelerated degradation analysis and traditional accelerated life test failure-time analysis.

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