A Method to Reduce Rotorcraft Development Risk by Integrating Historical Quantitative Risk Assessment into Fault Tree Models
Bibliographic record
Abstract
Fault Tree Analysis (FTA) is performed during vertical lift product development, but only an estimation of the probability of component failures can be made at that point of product design and development. Estimation of component failure probability during FTA typically does not account for component aging, installation effects, maintenance actions, and other factors encountered in operation, which can lead to under prediction, resulting in identification of hazards during test, evaluation, and deployment. Quantitative Risk Assessment (QRA) is typically performed during fleet operation. Efforts to eliminate hazards or mitigate risks are less effective and much more costly in this phase of the product lifecycle compared to proactively addressing hazards early in development. If the risk of failures could be accurately predicted earlier, hazards could be addressed early in the process. Such a method is presented here, where historical QRA for similar hazards can be integrated into the FTA. This would reduce cost, schedule, and safety risks by reducing the risk of failure during ground and flight test and in fleet operation.
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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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".