Importance Analysis of Components of a Multi-Operational-State Power System Using Fault Tree Models
Bibliographic record
Abstract
This article describes a case study using a fault tree analysis for a multi-operational-state system (system with several operational states) model with many different technical solutions for the power system of a fishing vessel. We describe the essence of system dependability metamodeling. A vector of external events was used to construct a detailed metamodel, depending on the operational status being modeled. In a fault tree, individual external events modify the structure of a system. The analysis includes the following operational states: sea voyages of a vessel, hauling in and paying out nets, trawling, staying in a port, and heaving to. For each operational state and assumed system configurations, the importance of system components was determined by calculating the Vesely–Fussell measures. The most important components for each operational state of a system were determined, and the critical system components, that is, those that are important in every operational state and system configuration, were identified.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".