An Accelerated Life Test Model Based on Reliability Kinetics

Inferences from accelerated life tests typically involve extrapolation in stress. For this reason, it is important to use statistical models that have their basis in the physics and chemistry of the important failure mechanism(s). The standard accelerated failure time regression models (based for ex...

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Bibliographic Details
Published in:Technometrics 1995-05, Vol.37 (2), p.133-146
Main Authors: Meeker, William Q., LuValle, Michael J.
Format: Article
Language:English
Subjects:
Anions
Applied sciences
Censored data
Copper
Differential equations
Electronics
Exact sciences and technology
Humidity
Kinetics
Life data analysis
Mathematical extrapolation
Maximum likelihood
Modeling
Parametric models
Printed circuits
Regression analysis
Reliability
Test data
Testing, measurement, noise and reliability
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Summary:Inferences from accelerated life tests typically involve extrapolation in stress. For this reason, it is important to use statistical models that have their basis in the physics and chemistry of the important failure mechanism(s). The standard accelerated failure time regression models (based for example on Weibull or lognormal approximations to the failure-time distribution at a given stress and linear scaling of time) are adequate for modeling some simple chemical processes that lead to failure. In this article we present the results from a humidity-accelerated life test of conductive anodic filament failures on printed circuit boards. Standard accelerated life test models are clearly inadequate for these data. Using an approximate chemical kinetic model of the failure process as a basis, we derive an alternative, more general, class of accelerated life test models. We illustrate the use of likelihood-based methods to estimate the model parameters. The new models fit the data better than the traditional accelerated life test models and provide extrapolations that are more consistent with actual field data.
ISSN:0040-1706
1537-2723
DOI:10.1080/00401706.1995.10484298