Integration of the Functional Hazard Assessment Within a Model-Based Systems Engineering Framework
Bibliographic record
Abstract
A major challenge in developing novel aircraft concepts is demonstrating the safety of increasingly complex and multifunctional aircraft systems. Aircraft manufacturers are adopting model-based systems engineering approaches to develop these new aircraft. The safety assessment process follows suit with model-based safety assessment. However, system and safety engineers still transfer information that is mainly document-based during the system architecting process. This paper aims to improve this process. First, a framework for developing system architecture specification models is introduced using the Architecture Analysis and Design Integrated Approach (ARCADIA)/Capella methodology and tool, illustrated with an aircraft landing gear braking system. Secondly, the paper proposes enhancements to the system specification model to enable functional hazard assessment and to capture the results within the system architecture specification model, i.e., using color-coding of system functions according to the severity of their associated failures as a visual aid to the system architect. In addition, the proposed features in the system specification model can help the safety engineer analyze failure relationships better. In summary, the proposed method improves consistency between the system architect and the safety expert in making safety-informed architecting decisions early in the development process, improving its effectiveness.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".