Online monitoring of the distributed lateral displacement in large AC power generators using a high spatial resolution Brillouin optical fiber sensor
Bibliographic record
Abstract
We report for the first time, to the best of our knowledge, online monitoring of the distributed lateral displacement in large AC power generators using high spatial resolution differential pulse-width pair Brillouin optical time-domain analysis (DPP-BOTDA). To perform the measurement of distributed lateral displacements with periods of only a few cm in large AC power generators, a 2 cm spatial resolution strain measurement is realized using the differential pulse pair of 8/8.2 ns in DPP-BOTDA, and then the lateral displacements are reconstructed according to the strain–displacement relation with the assumption of a sine shape function. Using different fiberglass ripple springs, two types of lateral displacement with periods of 3 and 3.25 cm are demonstrated, obtaining a maximum displacement of 0.43 mm with a measurement accuracy of ∼ 40 µm. This provides the information on the stator coil tightness through online monitoring of the distributed lateral displacement caused by the fiberglass ripple springs, and ensures safe operating conditions for large AC power generators. In addition, the large number of sensing points associated with distributed optical fiber sensors make it economically and technically practical to monitor large numbers of key components in a generator without any interference from the large magnetic and electrical fields.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".