Development of A Portable Instrument for Detecting Partial Discharge by Ultrasonic Beamforming Technique
Bibliographic record
Abstract
In this paper, we introduce partial discharge visualization technology. To monitor partial (external corona or creeping) discharge at exposed parts remotely, detection of ultrasound emitted from partial discharge is frequently carried out. Beamforming technique, in which ultrasonic waves measured by multiple microphones are analyzed, was adapted for determining an arrival direction of ultrasound. By superimposing arrival direction of ultrasound on video image of an objective apparatus recorded by a camera, we can promptly know position where partial discharge occurs in wide area to be monitored. A small and lightweight instrument called “Corona Discharge Viewer™ MK-760” was developed for on-site partial discharge detection. The beamforming computing is carried out repeatedly in real-time, and location of partial discharge is shown with visual image on the LCD display. Therefore, we can easily detect and locate partial discharge by just moving MK-760 facing towards to objective apparatus without any esoteric operations. Corona discharge occurred at an insulator and a bend section of a twisted bare wire of actual electric transmission facilities were satisfactorily detected and located by using MK-760.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".