Classification of Defects in Outdoor Ceramic Insulators using Machine Learning
Bibliographic record
Abstract
Outdoor ceramic insulators are widely used in both transmission and distribution overhead lines. Many of these insulators either exceeded or approaching the end of their expected life. The failure of outdoor insulators can lead to significant economic losses. Hence, it is paramount to develop non-intrusive techniques to detect the likely hood of outdoor insulators failures. This paper presents a system combining an ultrasonic sensor with a bandwidth between 20-100kHz and a machine learning classification model to detect various defects in ceramic insulators, including internal defects, pollution, and corona. The approach involves extracting both statistical and spectral features from the time-domain ultrasonic signals. Key features are selected using Causality Analysis and Information Gain, focusing on the top 10 most significant features. K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB) have been used as classifiers. The system demonstrates an overall classification accuracy exceeding 93% across four different machine learning models.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".