Integration of Degradation Models Into Generation Risk Assessment (GRA): Challenges and Modeling Approaches
Bibliographic record
Abstract
The main objective of generation risk assessment (GRA) is to assess the impact of equipment unavailability and failures on the ability of the plant to produce power over time. The system reliability models employed for this purpose are based on the standard fault tree /event tree approach, which assumes failure rates to be constant. However, this ignores the impact of aging degradation and results in static estimates of expected generation loss. Component and equipment degradation not only increases the probability of failure over time, but also contributes to generation risk through increased unavailability and costs arising from greater requirement for inspection and replacement of degraded components. This paper discusses some of the key challenges associated with integrating the results of component degradation models into GRA. Because many analytical and simulation methods are subject to limitations, the methodology and modeling approach proposed in this work builds on the current GRA practice using the fault tree approach. The modeling of component degradation can be done separately at the fault tree cut set level, assuming the cut sets are independent and the component unavailabilities are relatively small. In order to capture the joint contribution of equipment failure and unavailability to generation risk, new risk-based importance measures are also developed using the concept of net present value (NPV).
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".