Applying System-Theoretic Accident Model and Processes (STAMP) to Hazard Analysis
Bibliographic record
Abstract
Although traditional hazard analysis techniques, such as failure modes and effect analysis (FMEA), and fault tree analysis (FTA) have been used for a long time, they are not well-suited to handling modern systems with complex software, human-machine interactions, and decision-making procedures. This is mainly because traditional hazard analysis techniques rely on a direct cause-effect chain and have no unified guidance to lead the hazard analysis. The Systems Theoretic Accident Model and Process (STAMP) is based on systems theory to try to find out as much as possible about the factors involved in a hazard, and with providing clear guidance as to the control structure leading to the hazard. The Darlington Nuclear Power Generating Station was the first nuclear plant in the world in which the safety shutdown systems are computer controlled. Although FTA and FMEA have already been applied to these shutdown systems, Ontario power generation felt that it is still useful to try recent advances to evaluate whether they can improve on the previous hazard analysis. This thesis introduces the two most common traditional techniques of hazard analysis, FTA and FMEA, as well as two systemic techniques, STPA (which is a hazard analysis method associated with STAMP), and the Functional Resonance Accident Model (FRAM). The thesis also explains why we chose STPA to apply to the Darlington Shutdown System case, and provides an example of the application as well as an evaluation of its use compared with FMEA and FTA.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".