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Record W4386309331 · doi:10.1002/iis2.13031

MBFHA: A Framework for Model‐Based Functional Hazard Assessment for Aircraft Systems

2023· article· en· W4386309331 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 · 2023
Typearticle
Languageen
FieldEngineering
TopicSafety Systems Engineering in Autonomy
Canadian institutionsSafran Electronics (Canada)University of Toronto
Fundersnot available
KeywordsAerospaceWorkflowConsistency (knowledge bases)Computer scienceSystems engineeringKey (lock)HazardHazard analysisSystems Modeling LanguageReliability engineeringSoftware engineeringUnified Modeling LanguageEngineeringDatabaseSoftwareAerospace engineering

Abstract

fetched live from OpenAlex

Abstract To address growing system complexity in the aerospace industry, a Model‐Based Systems Engineering (MBSE) approach has been increasingly adopted for the development of aircraft systems. This calls for a corresponding approach for performing safety assessment to maintain consistency between the system and safety domains. One of the key safety assessment processes for aircraft development is the Functional Hazard Assessment (FHA). The purpose of this paper is to build upon previously published works and introduce the MBFHA framework which describes the language, method, and tool needed for implementing a model‐based approach to performing FHA and integrating it into MBSE activities. A customised FHA profile is introduced for the modelling language, an overall workflow along with processes for FHA report and safety requirements generation is presented for the method, and a list of tool constraints is provided. A proof‐of‐concept is subsequently presented using safety data for the landing gear extension and retraction system of a generic business aircraft.

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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.277
Teacher spread0.256 · 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