Integrating Functional Block Diagrams and Systems-Theoretic Process Analysis: A Case Study of a Nuclear Power Station
Bibliographic record
Abstract
Hazard analysis and risk assessment are critical for ensuring safety and reliability in complex systems.This article presents a combined approach to hazard analysis and risk assessment using Functional Block Diagrams (FBD) and Systems-Theoretic Process Analysis (STPA) methods.The FBD method is a versatile and intuitive diagrammatic technique used to describe the functions and interrelationships of complex systems.It represents the system as a set of interconnected blocks, each depicting a specific function, which collectively defines the system's behaviour.On the other hand, STPA is an advanced safety analysis method focusing on control structures and the interaction between components.It identifies potential unsafe control actions by analysing the information flow and the system's feedback mechanisms.The two methods are complementary and can be integrated to provide a more effective and efficient approach to hazard analysis and risk assessment.A case study of a nuclear power plant is used to demonstrate the benefits of the combined approach.Practical considerations for implementing the approach are discussed and compared with other hazard analysis and risk assessment methods.The article concludes with suggestions for future research and development in this area, highlighting the potential impact of the combined FBD-STPA approach for improving safety and reliability in complex systems.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".