Enhancing Transformer Health Index Prediction Using Dissolved Gas Analysis Data Through Integration of LightGBM and Robust EM Algorithms
Bibliographic record
Abstract
The dissolved gas analysis (DGA) data play a crucial role in evaluating the transformer health index (HI). In recent years, data-driven approaches have attracted significant research interest for the HI prediction with various health condition data. However, the DGA data collection is prone to missing or erroneous data due to sensors or data transfer issues. Consequently, handling missing data requires careful attention for accurate HI computation. In this paper, a novel data-driven hybrid approach is proposed that leverages the Light Gradient Boosting Machine (LightGBM) as a regression method and the Robust Expectation-Maximization (robust-EM) as a missing data imputation technique to predict the HI of transformers using DGA data. The proposed method is evaluated through five case studies with the percentage of missing data at 0%, 5%, 10%, 15%, and 20%. The proposed method has been compared with seven benchmark methods through six evaluation metrics, showing superior performance. The proposed method is also analyzed with and without robust-EM, and 22% – 71% performance improvements across various case studies and performance metrics have been achieved with robust-EM.
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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 | 0.003 |
| 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".