Maintenance resource planning for utility poles in a power distribution network
Bibliographic record
Abstract
In this paper, we address the problem of maintenance resource planning for utility wood poles for a power distribution company. The poles are currently replaced with new ones either when they fail or are found in poor condition at regular inspections. As the poles age, a large number of failures might occur, yielding an unexpected increase in the demand for maintenance resources. Timely preventive replacement of poles is one strategy to prevent such an increase in maintenance demand. Therefore, changing the maintenance program such that poles whose ages exceed a threshold value are also replaced at regular inspections can reduce the number of failures in the future and consequently the unplanned demand for maintenance resources. However, determining the threshold age is challenging. To solve the problem, we assume that the failure time of poles follows a Weibull distribution and estimate its parameters by the maximum likelihood method from the available left truncated and right censored data. To justify the necessity of preventive replacement, we then use the delayed renewal process theorem to calculate the expected number of failures in any given interval in the future assuming poles are replaced only at failure. Finally, we propose a mathematical programming model to determine the threshold age ensuring that the expected number of failures in a given future interval is limited. The methodology developed in this paper can be used by any utility to limit the number of unplanned replacements.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".