Adapting the FBG cavity sensor structure to monitor and diagnose PD in large power transformer
Bibliographic record
Abstract
Fiber optics vibration sensors have been extensively studied over the last ten years they have mainly been used to capture low frequency signals. One of these investigations has been commercialized to measure and trend End Winding Vibration (SEW) inside larger power generators. This sensor consists of a twin grating cavity and can be used to monitor temperature change as well as vibration. This paper will discuss the adaptation of the FBG cavity structure into a broad band sensor capable of measuring a range of higher frequencies previously identified as PD signals in the transformer from 30K to 300 KHz. Other work has been done to explore the possibility of developing a fiber optics acoustic sensor. The incentive is to overcome the limitation of the resonant based, acoustic, piezoelectric PD sensors previously used to analyze and locate PD. Working in resonance, they are unable to reveal the detailed spectrum or the signature of the PD events. We want to improve the packaging material and structure, then optimize the coupling methods to enhance its signal to noise ratio. All our work is still based on the fiber gratings cavity structure.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".