Transformer Fault Diagnosis Based on Stacking-Ensemble Meta-Algorithms
Why this work is in the frame
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Bibliographic record
Abstract
Compared with the method of establishing a single classifier for diagnosis, ensemble learning can combine multiple classifiers to achieve stronger generalization ability. This paper proposed a transformer fault diagnosis method based on Stacking Ensemble multiple classifiers, which can detect the transformer’s internal fault by using its DGA data. The proposed model is consisted of two sections. The first section includes five diagnosis models: Random Forest Classifier, AdaBoost Classifier, Gradient Boosting Classifier, SVM and Extra Trees Classifier. The second section use XGB Classifier as final Meta-Classifier model to classify the faults of transformers by using all the base level model diagnosis results as input. The diagnosis accuracy of the proposed method is 83.3%, which is better than other single Classification method.
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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