Enabling Automated Fault Tree Assessment to Support Aircraft Systems Architecting in Early Design Phases
Bibliographic record
Abstract
Novel aircraft configurations require early safety assessments to prevent costly rework and validate feasibility. At the conceptual design stage, the primary challenge lies in deriving meaningful safety insights through qualitative and quantitative assessment methods in a manner compatible with the low granularity of preliminary architecture specifications. This paper presents an approach to represent the system architecture using a graph-based architecture descriptor that supports the automated generation of fault trees and enables a system architect to rapidly evaluate and reconfigure candidate system architectures based on quantitative safety metrics. The fault trees are generated by transforming the graph-based model of the architecture into an AltaRica 3.0 model with the behavior of each architecture element modeled on the incoming and outgoing power type, control signal, or mass flow. An aircraft landing gear braking system case study demonstrates the automated fault tree assessment and rapid architecture reconfiguration based on safety insights garnered from the fault tree assessment. Overall, the processes described in this paper enable safety-driven exploration of system architectures in conceptual design.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".