Accurate strain measurements with fiber Bragg grating sensors and wavelength references
Bibliographic record
Abstract
Fiber Bragg grating sensors are one of many fiber optic sensor technologies that are currently being used in structural health monitoring systems. The sensors operate by detecting shif in the wavelength of relfected maxima due to applied strain. This paper studies a new fiber Bragg interrogation method that utilizes a swept wavelength laser in combination with wavelength references. These include a gas cell, which is used as the long term wavelength standard and an etalon used for accurate interpolation of peak wavelengths. An etalon is essentially a filter that has a periodic response over a broad wavelength range. Since its wavelength response spacing is smaller than the gas cell, it can be used to determine the intermediate wavelengths between two gas cell absorption lines. Peak location is a key element of this interogation method and several detection algorithms are investigated. It was determined that polynomial peak fitting is the most computationally efficient method and yields a resolution of better than 0.5 pm with signal to noise ratios of 30:1 or better. With higher signal to noise ratios, polynomial peak fitting can yield a resolution of better than 0.25 pm and a resolution of bettern than 0.25 pm. Using a tunable laser, a HCN gas cell and an etalon with maxima every 140 pm, static load tests will demonstrate that a resolution of 1 pm and an accuracy of less than 5pm can be achieved. Also, this accuracy will be maintained over a long period of time as it is based on absorption lines in the gas cell. The results of this study demonstrate that absolute accurate strain measurements can be obtained with the use of wavelength references in conjunction with a suitable peak location algorithm.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".