Economic and economic-statistical design of a multivariate Bayesian control chart for condition-based maintenance
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
It has been shown in the literature that Bayesian control charts are optimal tools for statistical process control but very few results have been published in this area. For any control chart, three control chart parameters must be specified, namely the sample size, the sampling interval and the control limit. Recently, industrial practitioners and academic researchers have increasingly recognized the cost benefits obtained by applying economically designed control charts to quality control, equipment condition monitoring and maintenance decision making. In this paper, design of a multivariate Bayesian control chart for condition-based maintenance (CBM) applications is considered using the control limit policy structure and including an observable failure state. Optimization models for the economic and economic statistical design of the multivariate Bayesian control chart for a three-state CBM model are developed and comparison results show that the multivariate Bayesian control chart performs better than the recently developed CBM chi-square chart.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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) | 0.000 | 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