Hydrophobicity classification of RTV silicone rubber-coated insulators using deep learning algorithms
Bibliographic record
Abstract
Outdoor polymeric insulators based on silicone rubber are widely used in power transmission and distribution networks. They exhibit several advantages like light weight, vandalism resistance and superior pollution performance due to their hydrophobic surface property. However, silicone rubber insulators suffer from aging that will lead to the loss of their hydrophobicity property. Various methods have been used to classify the hydrophobicity of the insulator surface including static and dynamic contact angle measurement and hydrophobicity class as per the IEC 62073 standard. The main goal of this paper is to use deep learning techniques to automatically assess the hydrophobicity classes (as per the IEC 62073 standards) of non-ceramic insulators under various conditions. A dataset of the hydrophobicity classes (HC1-HC6) was created including 4197 images each having 224×224 pixels size to train the proposed model. Convolutional Neural Networks (CNN) and Transfer Learning (TL) were used in this study to categorize and evaluate the hydrophobicity classes of ceramic insulators coated with room-temperature vulcanized silicone rubber. Compared to other CNN pre-trained models, the MobileNet model was found to have the highest accuracy and the lowest training time under several conditions, with maximum classification accuracy of 97.8%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".