Application of probabilistic health analysis in generating facilities maintenance scheduling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Preventive maintenance scheduling of generating facilities is an important requirement in generating system planning. Not conducting maintenance may enhance the ability to provide the available reserve in the short run, but will lead to higher generating unit failure rates which could create serious reserve shortages and decreased system reliability. A new technique designated as the health levelization technique is presented in this paper. This technique is a hybrid approach, which incorporates a deterministic criterion within a probabilistic framework. In the studies described in this paper, the probability of health is determined using the capacity of the largest unit. The maintenance schedules obtained using the health levelization technique is more responsive than the schedules obtained using the reserve levelization approach as it has the capability to incorporate many of the uncertainties that exist in the process. Deterministic techniques can create maintenance plans that satisfy the approved deterministic criteria. They can also create situations in some weeks in which there is excessive system risk due to the fact that the deterministic techniques do not involve any consideration of the actual risk.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it