Bibliographic record
Abstract
Risk Importance Measures (RIMS) obtained from both qualitative and quantitative aspects of Fault Tree (FT) analysis can be used to identify weak links in a system. Information from RIMS can be used to direct resources towards the components that deserve the most attention. When RIMS are used to make maintenance-related decisions, it is referred to as risk-informed maintenance. Risk importance analysis for coherent FT has received much attention over the years. However, non-coherent FT does occur in real systems due to either the nature of the system or poor design. Non-coherent FT introduces difficulties in terms of both qualitative and quantitative assessment, and the importance analysis of noncoherent FT is rather limited. In this paper, eight most commonly used RIMS are investigated and extended to noncoherent forms. They are the Birnbaum's Measure (BM), Criticality Importance Factor (CIF), Improvement Potential (IP), Fussell-Vesely Measure (FV), Risk Achievement (RA), Conditional Probability (CP), Risk Achievement Worth (RAW) and Risk Reduction Worth (RRW). The feasibility of the extension are proved and presented throughout the analysis and applications. Furthermore, they are classified with respect to risk significance and safety significance. The CIF, IP, FV and RRW are identified as risk significant measures, while BM, RA, CP and RAW are identified as safety significant measures. Since maintenance can normally be categorized as corrective maintenance and preventive maintenance, it is concluded that risk significant measures contribute most information to corrective maintenance and safety significant measures contribute most information to preventive maintenance. An Automatic Power Control System (APCS) for an experimental nuclear reactor is used as a case study to demonstrate the theoretical development.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".