Bibliographic record
Abstract
The reliability indices of the turbines were analyzed based on a six-year failure database. Such reliability indices as failure rate (), repair rate () and mean time to repair () have been estimated. The analyses showed that gas turbine 1 (GT1) has maximum failure rate () of once in 100 h in 2007 with system availability () of 0.333 and minimum failure rate () of once in 1000 h in 2005 with of 0.983. While for gas turbine 2 (GT2), of once 100 h was obtained in 2007 with of 0.333 and of once in 1000 h in 2008 with of 0.0869. It was also showed that GT1 has a maximum repair rate () of once in 1.45 h in 2005 with of 0.983 and minimum repair rate () of once in 21.28 h in 2006 with of 0.513. In the other hand, GT2 has of once in 1.44 h in 2008 with of 0.869 and of once in 35.7 h in 2006 with of 0.513. GT1 has maximum mean time to repair () of 2133 h in 2006 with of 0.595 and minimum mean time to repair () of 72.5 h in 2005 with of 0.714. Similarly, GT2 has of 3552 h in 2006 with of 0.595 and of 144 h in 2008 with of 0.984. For the period under study, GT1 has of 0.333 in the year 2007 and of 0.983 in 2005 while GT2 has of 0.595 in 2006 and of 0.984 in 2008. Measures to improve the reliability () indices of the plant have been suggested such as training and retraining of technical personnel on the major equipment being used. Key words: Warri Refining and Petrochemical Company (WRPC) gas turbines, reliability indices, maintainability.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".