Identifying, Assessing and Prioritizing Pressure Reduction Station Risks Using FMEA Based on SWARA-VIKOR Multi-criteria Decision-making Methods
Bibliographic record
Abstract
Introduction: Failure modes and effects analysis (FMEA) method is used in industries to identify, assess and prioritize risks. Multi-criteria decision-making methods (MCDM) select the best option from different criteria. Therefore, this study aims to identify, assess and prioritize risks using FMEA based on SWARA-VIKOR multi-criteria decision-making methods in a gas pressure reduction station. Material and Methods: In this descriptive and analytical study, stepwise weight assessment ratio analysis (SWARA) and decision-making optimization and compromise solution (VIKOR) methods were used to rank the risks of failure modes identified in FMEA. SWARA method was employed to determine the severity, probability and discovery weights, and VIKOR technique was applied to rank the failure modes of the system equipment. Finally, an operational example of the pressure reduction station was presented to show the application and feasibility of the proposed model. A comparative study was conducted to confirm the practicality and effectiveness of the proposed model. Results: In total, 35 main failure modes were identified in the pressure reduction station. Failure of regulator sleeve and safety valve and regulator diaphragm rupture were assigned the first, second and third ranks of risk priority, respectively. The sensitivity analysis results showed the proposed approach had desirable stability and only the failure mode of increasing the heater flame temperature was very sensitive to changes in the weight of the criteria. Results of ranking the failure modes of the station indicated there were many changes in the ranking of failure modes based on the proposed approach. Conclusion: The proposed approach could provide more reasonable and accurate results for ranking risks because the criteria were weighed step by step based on the experts’ opinion.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".