Optimal inspection and preventive maintenance policy for systems with self‐announcing and non‐self‐announcing failures
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
Purpose The purpose of this study is to propose and model an inspection and preventive maintenance policy for randomly failing systems that alternate operating and idle periods according to their mission profile. Design/methodology/approach A maintenance policy is defined and modeled mathematically. The paper focuses on finding the age T for inspection which maximizes the stationary availability of the system. Findings Except for the case of only self‐announcing failures, there always exists a finite optimal strategy T *. Two sufficient conditions for the uniqueness of such an optimum are also derived. Practical implications Many productive systems alternate operating and inactive periods, their failures may be self‐announcing or not self‐announcing (detected only through inspection). This paper presents a maintenance strategy for such systems in order to maximize their stationary availability. The proposed strategy suggests submitting the system to inspection when its age reaches T units of time. Originality/value This paper states a general expression of the system stationary availability which is considered as the performance criterion. Conditions of existence and uniqueness of an optimal strategy are developed.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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