Combining and comparing various machine‐learning algorithms to improve dissolved gas analysis interpretation
Bibliographic record
Abstract
Since the discovery of dissolved gas analysis (DGA), it is considered as a leading technique for the diagnosis of liquid insulated power equipment. However, accurate analysis results can only be achieved if the measured gases closely reflect the actual equipment condition to enable an appropriate interpretation of these gases. In general, conventional techniques such as the ratio method, key gases, and Duval triangle combined or not with artificial intelligence techniques such as machine‐learning algorithms are used for DGA interpretation. Here, four well‐known machine‐learning algorithms are compared in terms of DGA fault classification – Bayes network, multilayer perceptron, k ‐nearest neighbour, and J48 decision tree. Moreover, the effect of applying ensemble methods such as boosting through the Adaboost algorithm and bootstrap aggregation (bagging) is analysed, and the performances of these algorithms are evaluated. The data for developing classification models was transformed into three forms, other than the raw data. The obtained results clearly presented the efficiency and stability of some algorithms such as the J48 tree and Bayes networks for DGA fault classification, in particular, when the data is appropriately pre‐processed. Moreover, the performance of these algorithms was found to consistently improve by integrating the concepts of multiple models or ensemble methods.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".