Data-Driven Model Selection Study for Long-Term Performance Deterioration of Gas Turbines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Performance of gas turbine engine (GTE) deteriorates with structural aging. The availability of operating data from GTE and capability to perform data analysis, provides an opportunity to identify long-term performance deterioration and relate to more difficult to detect structural degradation. In this work, performance analysis of a low power rating and partially loaded industrial GTE was carried out by using a model-free data analytic approach. A performance index (ratio of power generation to fuel consumption) is proposed as the metrics for monitoring the engine performance, and monitor the long-term degradation symptom. A comparative model selection study has been conducted among three multivariable models to select the best model describing long-term performance deterioration of the GTE.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it