Fiber Bragg Grating Wavelength Drift in Long-Term High Temperature Annealing
Bibliographic record
Abstract
High-temperature-resistant fiber Bragg gratings (FBGs) are the main competitors to thermocouples as sensors in applications for high temperature environments defined as being in the 600-1200 °C temperature range. Due to their small size, capacity to be multiplexed into high density distributed sensor arrays and survivability in extreme ambient temperatures, they could provide the essential sensing support that is needed in high temperature processes. While capable of providing reliable sensing information in the short term, their long-term functionality is affected by the drift of the characteristic Bragg wavelength or resonance that is used to derive the temperature. A number of physical processes have been proposed as the cause of the high temperature wavelength drift but there is yet no credible description of this process. In this paper we review the literature related to the long-term wavelength drift of FBGs at high temperature and provide our recent results of more than 4000 h of high temperature testing in the 900-1000 °C range. We identify the major components of the high temperature wavelength drift and we propose mechanisms that could be causing them.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".