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Integrating Safety Analysis into Model‐Based Systems Engineering for Aircraft Systems: A Literature Review and Methodology Proposal

2021· review· en· W3200032009 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueINCOSE International Symposium · 2021
Typereview
Languageen
FieldEngineering
TopicSafety Systems Engineering in Autonomy
Canadian institutionsSafran Electronics (Canada)University of Toronto
Fundersnot available
KeywordsSystems Modeling LanguageSystems engineeringComputer scienceProcess (computing)Interface (matter)Unified Modeling LanguageSoftware engineeringModeling languageEngineeringProgramming language

Abstract

fetched live from OpenAlex

Abstract Model‐Based Systems Engineering (MBSE) has become increasingly popular within the aircraft industry in recent years. However, this model‐based approach presents a challenge as traditional safety analysis practices are unable to keep up, resulting in inconsistency between the system and safety domains. This paper proposes a methodology tailored towards aircraft systems that addresses this issue by integrating safety analysis into MBSE. This is achieved by extending the Systems Modeling Language (SysML) profile to account for safety data in the system model and utilizing an Application Programming Interface (API) to automate the generation of safety analysis artefacts. The proposed methodology also allows for requirements management integration to increase the efficiency of the system development process.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.419
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.317
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it