An optimal maintenance policy for skipping imminent preventive maintenance for systems experiencing random failures
Bibliographic record
Abstract
In this study we investigate systems that experience random failures and establish decision rules for performing renewal maintenance; that is, a preventive replacement (PR) policy. We seek a policy that is both simple to execute from the point of view of the maintenance planner but also a policy that is an improvement on existing schemes. We show that our policy is a hybrid of traditional time-based and age-based schemes and one that yields considerable cost savings. Our hybrid policy involves two decision variables. One decision variable is the time between PRs. Hence, for the maintenance planner, the times at which PRs are performed are chronologically fixed. Random failures can occur, however, and the machine receives an emergency renewal (ER) at these times. Hence, within these chronological times, a second decision time is identified. Should an ER occur between the start of a cycle and this second decision time, then the planned PR would still be performed at the end of the cycle. However, if the first ER occurs after this second decision time, then the PR at the end of the cycle is skipped over and the next planned PR would take place at the end of the subsequent cycle. With this simple mechanism, PRs that follow on too closely after an ER are avoided, thus saving the unnecessary expense. Numerical examples are given to examine the validity of the model, using four different failure density functions, namely Weibull, normal, uniform, and negative exponential.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".