Gas Turbine Fault Diagnosis Based on Machine Learning Techniques
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a method of diagnosis and prognosis of the imbalance fault of a gas turbine using machine learning techniques by putting its advantages over the old diagnostic procedures, mainly temporal analysis and frequency analysis of signals. Therefore, this work proposes a predictive maintenance approach to monitor and detect in realtime whether the power turbine is subject to an imbalance or is in normal operating condition using artificial intelligence techniques. For this purpose, a comparative study of three machine-learning algorithms, Support Vector Machine (SVM), Random Forest (RF), and K Nearest Neighbors (KNN), has been carried out. The results show that both RF and KNN have the best performance, with an F1 score of 99.95% and 99.97%, respectively. For the Linear SVM, we obtained an F1 score of 61.73%.
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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