Echelle Diffractive Grating Based Wavelength Interrogator for Potential Aerospace Applications
Bibliographic record
Abstract
Operational load monitoring and impact damage detection are the two critical aspects of aerospace structural health monitoring (SHM). Fiber Bragg grating (FBG) sensors have demonstrated great potential in both. But the currently available interrogation systems can only handle one of the two types of SHM capabilities offered by FBG sensors. In addition, the practical implementation of FBG sensor systems in aerospace vehicles requires the interrogator to be small size, light weight, and low-power consuming. In this paper, we present an Echelle diffractive grating (EDG) based interrogation system for FBG sensors, which possesses two operation modes, i.e., the sweeping mode for operational load monitoring and the parked mode for impact damage detection. Experimental results show that this interrogator offers better than 1-pm measurement resolution and 10-pm repeatability. In addition, the interrogation system is very compact and weighs less than 60 g (excluding the electronic controller). It also has the potential to achieve a measurement speed of 300 kHz and be powered by a battery.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".