Generator Insulation-Aging On-Line Monitoring Technique Based on Fiber Optic Detecting Technology
Bibliographic record
Abstract
The relationship between insulation aging and generator lifespan using fiber optic sensors (FOSs) is explored to ultimately improve asset lifespan through smart choices in running conditions and maintenance. Insulation aging is a major factor that causes generator failure. FOS provides the rare opportunity of being installed up close to the insulation, monitoring degradations that are otherwise difficult to detect. FOSs, unlike purely electrical transducers, are immune to high voltage (HV) and strong electromagnetic (EM) fields. They are small and have a proven long life by their deployment in the Telecom industry. The proposed FOS is a Fabry-Perot cavity made up of two identical fiber Bragg gratings (FBGs) using light wave interference as the working principle. Such architecture delivers simultaneous vibration (10 Hz–1 kHz) and temperature (0.1°C resolution) monitoring, both helping to spot irregular vibration patterns (signatures) and hot-spots inside the generator stator slots. The signal processing unit equipped with a gateway device can help to connect the large volume of sensor data, allowing correlation with the supervisory control and data acquisition (SCADA) system data of the plant. This chapter also elaborates on the field test jointly conducted with Calpine Corporation and Oz Optics, Ltd. (Ottawa, Ontario, Canada).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".