Nonintrusive Ultrasonic Sensing and Deep Learning for Outdoor Ceramic Insulator Assessment
Bibliographic record
Abstract
Insulator failure, often triggered by contamination flashovers and punctures, poses significant risks, leading to substantial technical and economic losses. Ceramic insulators are particularly vulnerable due to their hydrophilic and brittle properties, which compound these risks, thereby impacting the reliability of the electrical grid. This study introduces a novel approach that utilizes ultrasonic sensors in conjunction with a Convolutional Neural Network (CNN) to detect and classify defects in ceramic insulators. The model is trained using ultrasonic signals obtained from a defective two-disc configuration under controlled laboratory conditions, then evaluated with a three-disc configuration across various settings, including a real-world 138/13.8kV substation environment. The model demonstrates high accuracy, achieving rates of 99% and 96% for laboratory settings, and 91% in field conditions. Furthermore, through the integration of Gradient-weighted Class Activation Mapping (Grad-CAM), the model’s decision-making process is explored, revealing a focus on higher frequency components within the ultrasonic spectrum. This insight underscores the potential of deep learning in enhancing non-intrusive insulator assessment techniques, paving the way for more reliable electrical grid operations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".