Automatic Derivation of Fault Tree Models from SysML Models for Safety Analysis
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it