The Integration of Model-Based Safety Analysis and Model-Based Systems Engineering at an Early Stage
Bibliographic record
Abstract
Two methodologies used in aircraft system developments are Model-Based Safety Analysis (MBSA), which assesses the safety risk associated with design in later stages, and Model-Based Systems Engineering (MBSE), a process that creates the domain models to help design a system. With the continuous growing use of both methodologies, it is inevitable that they will become linked to improve the design process from many aspects. This review identifies and proposes potential links for the integration of MBSA and MBSE by using the V-model of MBSA, thereby resulting in more effective design processes and reduced development costs. The paper addresses a general interpretation of the topic and supplementary case studies within the industry regarding both methodologies. Some benefits of the link between MBSA and MBSE include: utilisation of a wider range of analysis tools, automated communication of important definitions, consistency between both ends, and a potential improvement in confidence regarding design. As this is a preliminary proposal regarding the best approach for using MBSA in combination with MBSE, further research should be performed into the areas of formalized language, defining systems requirements for the usage of this approach, and relationships with the existing regulations and compliance needs. Deux méthodes utilisées dans le développement des avions sont l’Analyse de sécurité basée sur des modèles (ASBM), qui évalue les risques de sécurité associés avec la conceptualisation d’étapes antérieures, et l’Ingénierie des systèmes basée sur des modèles (ISBM), un processus qui crée des modèles de domaine pour concevoir un système. Avec l’utilisation croissante des deux méthodes, il est inévitable qu’elles seront liées pour améliorer le processus de conceptualisation de plusieurs aspects. Cette revue identifie et propose des liens potentiels qui pourraient intégrer l’ASBM et l’ISBM, en utilisant le model-V de MBSA, résultant ainsi en des processus de conceptualisation plus efficaces et des coûts de développement réduits. L’article adresse une interprétation générale du sujet et des études de cas supplémentaires dans l’industrie qui concernent les deux méthodes de développement. Certains des avantages du lien entre ASBM et ISBM incluent: l’utilisation d’une grande gamme d’outils d’analyse, la communication automatisée de dé nitions importantes de conceptualisation, la cohérence entre les deux extrémités, et une confiance améliorée dans la conceptualisation. Comme ceci est une proposition préliminaire concernant la meilleure approche pour combiner l’ASBM et l’ISBM, de plus amples recherches devraient être menées dans les domaines du langage formel, les exigences des systèmes pour utiliser cette approche, les relations avec les règlements actuels et les exigences pour la conformité.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".