Periodic Inspection Optimization Models for a Repairable System Subject to Hidden Failures
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Bibliographic record
Abstract
This paper proposes a model to find an optimal periodic inspection interval over finite and infinite time horizons for a multi-component repairable system subject to hidden failures. The components' failures can only be rectified at periodic inspections, when a failed component is either minimally repaired, or replaced with some age dependent probabilities. We calculate the excepted cost with delayed replacement or minimal repair of a component. Recursive procedures are developed to calculate probabilities of failures in every interval, expected number of minimal repairs, and expected downtimes for optimization over finite and infinite time horizons. Numerical examples of the calculation of the optimal inspection frequencies are given. The data used in the examples is adapted from a hospital's maintenance data for a general infusion pump.
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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 | 0.000 |
| 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