Reliability Calculation for Dormant k-out-of-n Systems with Periodic Maintenance
Bibliographic record
Abstract
In this paper, a dormant k-out-of-n systems redundancy calculation will be introduced. Dormant failure is a failure that cannot be detected when it occurs because of the nature of the failure characteristic. Therefore, a dormant failure becomes the blind point to the design for reliability and maintainability because of its inability to be detected. The most popular approach in detecting a dormant failure is to carry out a scheduled periodic inspection, test or maintenance activity. The scheduled periodic maintenance is applied to prevent and reduce the unexpected dormant failures that could lead to safety consequences, or costly corrective maintenance. This paper will introduce a methodology on how to calculate the reliability parameter such as Mean Time Between Failure (MTBF) for the dormant k-out-of-n redundant systems. The mathematical relationship between the effective MTBF and the scheduled periodic inspection/maintenance interval is also elaborated. Case studies are adopted to illustrate how to apply the developed reliability calculation methodology in the mass transit train reliability and safety design.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".