Strain measurement range enhanced chirped pulse φ-OTDR for distributed static and dynamic strain measurement based on random fiber grating array
Bibliographic record
Abstract
A high-performance distributed sensing system based on a random fiber grating array (RFGA) and multi-frequency database demodulation (MFDD) method for strain induced delay time measurement is demonstrated. It enables a wide measurement range for both static and dynamic strain sensing. The proposed MFDD method can enlarge the strain measurement range, since the large strain variation induced time domain trace distortion could be compensated for by laser initial frequency changes. Furthermore, a random fiber grating made with embedded large random refractive index changes along the single-mode fiber could provide a stable reflection with a wide reflection spectrum range. Such a structure successfully improves the time delay measurement precision and achieves a large tuning range, as demonstrated by the database in which a set of pre-recorded undisturbed reflected Rayleigh traces form RFGA at various laser frequencies. Ultimately, a dynamic strain with a peak-to-peak value of 12.5µε at a vibration frequency of 50 Hz is accurately reconstructed when the pulse repetition rate is 1 kHz, which was not detected using a conventional chirped pulse phase-sensitive optical frequency domain reflectometers. The maximum measurable strain variation of about 12.5µε represents a factor of 3 improvement. This number is limited by a pre-recorded frequency scanning range of RFGA response in the database.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".