Distribution Transformer Failure Prediction for Predictive Maintenance Using Hybrid One-Class Deep SVDD Classification and Lightning Strike Failures Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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