A Hybrid Regression Model to Estimate Remaining Useful Life of Transformer Liquid
Bibliographic record
Abstract
The prediction of the remaining useful life (RUL) of transformer oil helps in condition monitoring and health monitoring of oil-filled power transformers. However, the prediction of RUL depends on the ageing condition of the insulation system. In this paper, a novel hybrid machine learning (ML)-based regression model is developed for predicting the RUL of the insulating oil in years. A total of 26 features have been taken from different chemical and physical properties and indices of mineral oil. Later, features are selected using the Pearson correlation coefficient and conditional mutual information-based feature selection (CMIFS) techniques. Finally, a hybrid algorithm consisting of support vector regression (SVR), k-nearest neighbor (k-NN), multiple layer perceptron (MLP), ridge regression (RR), ElasticNet, Adaptive Boosting (AdaBoost), and extreme gradient boost (XGBoost) are used to predict the RUL of the oil. The performance of the hybrid model is analyzed by root mean square error (RMSE), root mean square logarithmic error (RMSLE), mean absolute error (MAE), and correlation coefficient (R2). The comparison with the individual base regression algorithm showed that the hybrid model performed better. The present study adds to the arguments that data-driven intelligent monitoring systems are essential for the safe and efficient health monitoring of transformers.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".