Order of magnitude increase in resolution of optical frequency domain reflectometry based temperature and strain sensing by the inscription of a ROGUE (Conference Presentation)
Bibliographic record
Abstract
Optical frequency domain reflectometry (OFDR) has been investigated for two decades as a way to replace the sensing based on fibre Bragg gratings (FBG) currently used in most industries for those applications, using the intrinsic Rayleigh scatter of fibres instead. [1] OFDR allows completely distributed strain and temperature measurements along a fibre. The increase of backscatter using UV laser exposition was recently reported, and was found to increase the sensitivity in both temperature and strain sensing. [2] We present a technique allowing to increase by over 50 dB the backscattered signal amplitude, based on the writing of a Random Optical Grating by Ultraviolet or ultrafast laser Exposure, i.e. a very weak, random grating over the entire length of the fibre. This improvement is, to the authors’ knowledge, over 25 dB higher than what was previously reported for UV exposure for the same exposition power. [2] This ROGUE is generated by inducing phase noise during the continuous writing of a FBG using a Talbot interferometer. This leads to a grating with a very broad bandwidth regardless of the exposure length and greatly increases the signal without limiting the scanning bandwidth, resulting in no loss in resolution. Using these enhanced fibres, we obtained a noise level over an order of magnitude lower than using regular unexposed fibres, allowing measurements of smaller temperature variations. Fibres where such ROGUEs are inscribed also allow the use of a much smaller scanning bandwidth with similar accuracy, resulting in faster acquisition speed. REFERENCES [1] M. Froggatt and J. Moore, "High-spatial-resolution distributed strain measurement in optical fiber with rayleigh scatter," Appl Opt, vol. 37, no. 10, pp. 1735-40, Apr 1 1998. [2] S. Loranger, M. Gagne, V. Lambin-Iezzi, and R. Kashyap, "Rayleigh scatter based order of magnitude increase in distributed temperature and strain sensing by simple UV exposure of optical fibre," Sci Rep, vol. 5, p. 11177, Jun 16 2015.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".