Damped Vibration Analysis of Extrinsic Fabry-Perot Interferometric Sensors using Artificial Neural Networks
Bibliographic record
Abstract
Health monitoring of a structure entails regular strain sensing. Vibrational strain, characterized as functions of damped sinusoids, is a typical case of strain that can act on a structure. Past research has developed a demodulation technique, employing artificial neural networks (ANN) as the processing element, for extrinsic Fabry-Perot interferometric (EFPI) sensors, attached to a vibrating structure, exposed to un-damped sinusoidal strain. The work employed two ANN to perform the demodulation. The first ANN was trained to extract the harmonic content from the EFPI modulated output and the second ANN was trained to predict the maximum strain acting, from the predicted harmonic content, during a vibration event. This project extends the study to a damped sinusoidal strain acting on the sensor. The ANN demodulation system predicts the maximum strain level from the spectral content of the sensor output, during a vibration event. Instead of employing an ANN to extract the spectral content, as done in the past research, simple fast Fourier transforms (FFT) is used. This paper develops the demodulation technique using computer simulations. Results are presented for different ANN architectures employed. An algorithm fusion system is presented that shows an improved accuracy in maximum strain prediction.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".