Automatic Derivation of Fault Tree Models from SysML Models for Safety Analysis
Bibliographic record
Abstract
Safety Critical Systems (SCS) are those systems that may cause harm to the user(s) and/or the environment if operating outside of their prescribed specifications. Such systems are used in a wide variety of domains, such as aerospace, automotive, railway transportation and healthcare. In this paper, we propose an approach to integrate safety analysis of SCSs within the Model Driven Engineering (MDE) system development process. The approach is based on model transformation and uses standard well-known techniques and open source tools for the modeling and analysis of SCSs. More specifically, the system modeled with the OMG’s standard systems modeling language, SysML, is automatically transformed in Fault Tree (FT) models, that can be analyzed with existing FT tools. The proposed model transformation takes place in two steps: a) generate FTs at the component level, in order to tackle complexity and enable reuse; and b) generate system level FTs by composing the components and their FTs. The approach is illustrated by applying it to a simplified industry-inspired case study.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".