Measurement Plan Optimization for Degradation Test Design based on the Bivariate Wiener Process
Bibliographic record
Abstract
This article concerns the optimization of measurement plans in the design of bivariate degradation tests for bivariate Wiener processes. After describing an unbalanced measurement scheme for bivariate degradation tests, we derive the likelihood function and provide a method for estimating the model parameters that is based on maximum likelihood and least squares. From the corresponding Fisher information matrix, we deduce an important insight, namely that longer degradation tests and longer intervals between measurements in the test design result in more precise parameter estimates. We introduce a model for optimizing the degradation test measurement plan that incorporates practical constraints and objectives in the test design framework. We also present a search‐based algorithm to identify the optimal test measurement plan that is based on the aforementioned measurement rule. Via a simulation study and a case study involving the Rubidium Atomic Frequency Standard, we demonstrate the characteristics of optimal measurement plans for bivariate degradation test design and show the superiority of longer duration tests involving fewer samples compared to alternative designs that specify testing more samples over shorter periods of time. Copyright © 2013 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".