Prediction of exhaust gas temperature in GTE by multivariate regression analysis and anomaly detection
Bibliographic record
Abstract
Statistical multivariate linear regression technique has been applied in predicting exhaust gas temperature (EGT) for a small gas turbine engine using three independent input variables. Data collected earlier over three years (YR) of operational cycle are used for modeling, training, testing and validation of the models. Regression coefficients, probability and significance, R-square and RMSE values are considered for quantitative comparison between regressed and measured EGT data. R2and RMSE values are observed to be highest for intermediate period (intermediate cycle in YR2) while the two values are substantially low for YR3 data (end cycle). These two values are 0.22 and 2.23 for 2010 data as compared to 0.93 and 11.4 for YR2 data. The results obtained through this work are indicative of an anomalous situation and support our earlier findings by ANN technique.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".