Distribution Transformer Failure Prediction for Predictive Maintenance Using Hybrid One-Class Deep SVDD Classification and Lightning Strike Failures Data
Bibliographic record
Abstract
The distribution transformer in field monitoring data failure may be influenced by its maintenance history and risk index in a distribution network associated with keraunic level, average number of lightning strikes, and protection devices employed. Transformer failure is a rare event, and the number of “failed” labels is much smaller than that of “non-failed” labels. Therefore, the transformer failure prediction can be formulated as an anomaly detection or binary classification with an imbalanced dataset, which is challenging to handle. In this paper, we propose a novel distribution transformer failure prediction method through a hybrid one-class deep support vector data description (SVDD) that uses the synthetic minority oversampling technique (SMOTE) to handle the data imbalance between minority and majority class labels. Minimum redundancy maximum relevance (mRMR) is used as a feature selection technique to improve the model's accuracy. The proposed method uses the current condition data of transformers and the distribution network to predict transformer failure for the next year. Real-world field data for 15,066 distribution transformers is used to train and validate the proposed method. It shows superior performance when compared against five benchmark approaches.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".