A study on inspection schemes in optimal design of control charts for deteriorating processes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The non‐uniform inspection scheme obtained by the constant integrated hazard procedure overcomes the uniform scheme economically in optimal design of control charts. The comperative study is generalized in this paper to an optimization problem which looks for the optimal sampling points among all possible sampling schemes. The objective function is simplified here by modelling sequential time intervals as a family of functions of the first sampling interval, which also has been induced by the constant integrated hazard approach. The study demonstrates the model implementation through the economic design of and T 2 ‐Hotelling control charts, both under the two widely used process failure mechanisms, that is, Weibull and Chen distributions. A comprehensive numerical investigation illustrates the possibility of existence of sampling schemes which outperform the constant integrated hazard approach and emphasizes the necessity of further investigation into the solution procedure.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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