Adapting the FBG cavity sensor structure to monitor and diagnose PD and vibration sparking in large generator
Bibliographic record
Abstract
The FBG cavity sensor was invented to measure and trend End Winding Vibrations (SEW) inside larger power generators. The sensor consists of a twin grating cavity which can be used to monitor vibration as well as temperature change by changing the center wavelength of the transmitting laser to track the movement of the interference fringes. This paper will discuss the adaptation of this FBG cavity structure to measure much higher frequency signals like those found in Partial Discharge (PD) events. Special package design is necessary to maintain the signal to noise ratio as the high frequency PD signal propagates along the conductor inside the stator slots to the end windings. The challenge would be the coupling of these signals to our PD sensors. They will be mounted at the same locations where SEW sensors would be installed for end winding vibration. We would select the windings with the highest induced voltage hence most susceptible to PD. By trending the vibration amplitude related to loading, we would be able to diagnose each winding structure has become loose. These sensing channels would be correlated with PD amplitudes as well as their signature; more work would be required to relate the signature to the degradation process of the insulation layer. By measuring time of arrival differences at both ends of the generator, we can estimate which slot is associated with the discharge event. The addition of the PD sensor to vibration and temperature capability makes TG Guard a comprehensive solution for safeguarding the generator; its diagnostic capability would also shorten maintenance time and reduced the required resources. Owner would know exactly what to do by integrating all the trending data.
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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 | 0.001 |
| 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".