Optimal Sample Size Allocation for Accelerated Degradation Test Based on Wiener Process
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Degradation tests are widely used to assess the reliability of highly reliable products which are not likely to fail under traditional life tests or accelerated life tests (ALT). However, for some highly reliable products, the degradation may be very slow, and thus it seems impossible to have a precise assessment within a reasonable test time. In such cases, an alternative technique is to use higher stresses to extrapolate the product's reliability at the normal use stress. This is called an accelerated degradation test (ADT). In this article, motivated by a LEDs data, we discuss the optimal allocation problem under accelerated degradation experiment when a Wiener process is used to describe the product's degradation path. We derive the Fisher information and the approximate variance of the estimated mean‐time‐to‐failure (MTTF) under normal use. Three optimality criteria are defined and the optimal allocation of test units are determined. Finally, the LEDs data is illustrated to demonstrate the efficiency of the optimal allocation of test units.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it