Integration of systems design and risk management through model‐based systems development
Bibliographic record
Abstract
Abstract Model‐based systems engineering is a powerful methodology to develop safety‐critical systems. The use of the system model as a single source of truth for risk and dependability analysis results in a consistent and complete assessment. Besides, representation and logging of the assessment within the model result in a complete and up‐to‐date single source of information that can be used during the device certification as well. This paper aims to provide a comprehensive risk management SysML profile that includes interconnected safety analysis [functional hazard assessment (FHA), fault tree, and failure mode and effect analysis (FTA, FMEA)], control measure, and evaluation model elements in compliance with the medical standards. Model‐based risk assessment of a point‐of‐care diagnostic device for sepsis has been shown as a case study to show the implementation of the profile. This device is a standalone unit and the test results obtained directly affect the patient. Therefore, both the top‐down (FHA and FTA) and bottom‐up (FMEA) safety assessment methods have been used. Another objective of the study is to define a systematic and holistic method to perform fault tree analysis, not only from the system architecture models but also from the functional, activity, and sequence diagrams of the system model.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".