Contributions to accelerated destructive degradation test planning
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Abstract
Many failure mechanisms can be traced to
underlying degradation processes. Degradation eventually leads to a
weakness that can cause a failure for products. When it is possible
to measure degradation, such data often provide more information
than traditional failure-time data for purposes of assessing and
improving product reliability. For some products, however,
degradation rates at use conditions are so low that appreciable
degradation will not be observed in a test of practical time length.
In such cases, it might be possible to use some accelerating
variables (e.g., temperature, voltage, or pressure) to accelerate
the degradation processes. In today's manufacturing industries,
accelerated destructive degradation tests (ADDTs) are widely used to
obtain timely product reliability information. In designing an
experiment, decisions must be made before data collection, and data
collection is usually restricted by limited resources. Careful test
planning is crucial for efficient use of limited resources: test
time, test units, and test facilities. The basic goal in designing
an experiment is to improve the statistical inference for the
quantities of interest by selecting appropriate test conditions to
minimize or control the variability of the estimator of interest.
Generally, an ADDT plan specifies a set of testing conditions and
the corresponding allocations of test units to each condition. In
this dissertation, we study the test planning methods for designing
accelerated destructive degradation tests from three aspects,
including non-Bayesian and Bayesian methods. First, Chapter 2
presents the non-Bayesian methods for accelerated destructive
degradation test planning when there is only one failure cause for
the testing products. Second, Chapter 3 describes the non-Bayesian
methods for accelerated destructive degradation test planning when
more than one failure cause (sometimes known as competing risks) are
induced for the produces which are tested at high-stress levels of
accelerating variables. Third, Chapter 4 shows the Bayesian methods
for accelerated destructive degradation test planning.