Machine Learning-Based Condition Monitoring of Outdoor Insulators Via Acoustic Signals
Bibliographic record
Abstract
Ceramic insulators have been commonly utilized in overhead power lines for more than a century. Ceramic insulators in many installations are now nearing or going beyond their expected service durations. In this study, three commonly used machine learning algorithms have been compared in terms of outdoor insulator defect type classification: Support Vector Machine (SVM), Naive Bayes (NB) and Decision Tree (DT). The Discrete Wavelet Transform (DWT) method was utilized to extract features from the acoustic signals. The obtained feature sets were split into training and testing datasets. This approach allowed the classifiers to be trained and evaluated with datasets of varying sizes. The highest classification accuracy, 99.40 %, was achieved by the SVM algorithm when 20 % of the dataset was used for testing. Conversely, the lowest accuracy,$\mathbf{8 5. 4 1 \%}$, was recorded by the NB algorithm when 30% of the dataset was used as test data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".