Comparison of Two Methods for Evaluating the Effects of Maintenance on Component and System Reliability
Bibliographic record
Abstract
Abctroo- Two recently proposed methods for evaluating the effects of various maintenance policies on the reliability and operating costs of electric power systems are compared. Both programs, the one developed in Canada and the other in Sweden, first generate a priority list of components whose failures hnve the highest effect on system reliability, then examine the failure rates of the components on the top of the list-,hen various maintenance policies are applied. Finally, reliability indices for the whole system are computed with the critical components represented by the failure rates corresponding to the various policies. This way, maintenance policies can be selected either to achieve the highest system reliability or the lowest operating costs. The approach is illustrated in a numerical example where both the similarities of, and the daerences between, the two methods eau be recognized. Comments are made on the reasons for the differences. Index lerm- Power system reliability, Power distribution
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".