Reliability functions and optimal decisions using condition data for EDF primary pumps
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
There are many examples in which proportional hazards modelling (PHM) is used to accurately model the effects of the operating environment on an item's lifetime. Using such techniques it is possible to find an economically optimal moment of replacement based on condition monitoring data. However, in a nuclear power plant, the problem is somewhat different as it is not possible to stop the process and perform preventive maintenance at the most economical moment in time. The problem of interest in such cases has more to do with whether the item will last until the next scheduled plant shutdown or not. This paper presents some developed and implemented theory to calculate the conditional reliability function of an item given the current equipment age and the current values of the condition-monitoring variables. A case study of a shaft seal for a reactor cooling pump (RCP) using data from several French nuclear power plants operated by the Electricite de France (EDF) is also presented
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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