Influence of Statistical Distributions on Availability and Inspection Interval of Protective Devices
Bibliographic record
Abstract
Protective devices are designed to protect people, the environment and material assets under emergency situations. If protective devices do not work well, serious consequences may be resulted. It is critical to pay special attention to their maintenance. For this reason, many availability models have been developed to obtain an optimal inspection interval and to maximize their availability. However, few attention have been paid to the relationship between the statistical distributions used to describe the lifetime of protective devices and their optimal inspection interval and maximum availability. Furthermore, the problem that might occur when the normal distribution takes negative values has not been considered yet in protective device maintenance. This thesis aims to calculate the optimal inspection interval and maximum availability for the Weibull, normal, truncated normal and exponential distributions. Also, the relationship between these statistical distributions, and the availability and the inspection interval is studied. Finally, this thesis intends to study the problem that arises when the normal distribution might take negative values. To meet these objectives, an existing availability model, which considers constant time between inspections, is adapted to the Weibull, normal, truncated and exponential distributions. After adapting the model to each distribution, the effects of each distribution’s parameters on the optimal inspection interval and maximum availability are analyzed. It is not recommended to use the normal distribution if it has a large number of negative values while the truncated normal distribution is suggested as a possible approach to replace the normal distribution. This analysis help us to have a understanding on what is the performance and limitations of each of the four distributions.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".