Feature-Weighted MMD-CORAL for Domain Adaptation in Power Transformer Fault Diagnosis
Bibliographic record
Abstract
Ensuring the reliable operation of power transformers is critical to grid stability. Dissolved Gas Analysis (DGA) is widely used for fault diagnosis, but traditional methods rely on heuristic rules, which may lead to inconsistent results. Machine learning (ML)-based approaches have improved diagnostic accuracy; however, power transformers operate under varying conditions, and differences in transformer type, environmental factors, and operational settings create distribution shifts in diagnostic data. Consequently, direct model transfer between transformers often fails, and therefore, techniques for domain adaptation become a necessity. To tackle this issue, in this work, a feature-weighted domain adaptation technique is proposed, combining Maximum Mean Discrepancy (MMD) and Correlation Alignment (CORAL) with feature-specific weighting (MCW). Kolmogorov-Smirnov (K-S) statistics are utilized for assigning adaptable weights, prioritizing features with larger distributional discrepancies and therefore allowing for a better source and target domain alignment. Experimental evaluations over datasets for power transformers reveal the effectiveness of our proposed method, which attains a $7.9 \%$ improvement over Fine-Tuning and a $2.2 \%$ improvement over MMD-CORAL (MC). In addition, it outperforms both techniques under various training sample sizes, confirming its reliability in domain adaptation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".