An infrared spectrometer based on a MEMS fresnel zone plate for measuring dissolved gases in high voltage equipment
Bibliographic record
Abstract
This thesis presents a unique design for an infrared spectrometer based on a MEMS Binary Fresnel Zone Plate for the purpose of assessing the health of oil-impregnated high voltage (HV) equipment. It does so by measuring dissolved gases within it. These gases include carbon monoxide, carbon dioxide, methane, ethane, ethylene, and acetylene. These gases are currently measured using numerous technologies such as gas combustion, gas chromatography, photoacoustic spectroscopy, and FTIR spectroscopy. Each of these technologies have their advantages and disadvantages. The design presented in this thesis consists of an analysis of how the various Binary Zone Plate parameters affect its spectral resolution and transmission efficiency. Simulations show that increasing the number of zones and the focal length, as well as decreasing the aperture diameter, increases the spectral resolution of the spectrometer. Simulations also show that transmission efficiency is proportional to the number of zones and the aperture diameter. This thesis presents a theoretical argument for how one zone plate lens can be used to measure all dissolved gases present in HV equipment. Lenses for the visible and infrared ranges were fabricated in the University of Manitoba NSFL Cleanroom. The lenses were then tested in an optical setup. The results show that the visible light experiments were successful in achieving appropriate spectral discrimination by changing the distance between the aperture and the lens. The results from the infrared experiment show that a detector was able to discriminate between full and no incident radiation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".