A novel application of a laser Doppler vibrometer in a health monitoring system
Bibliographic record
Abstract
This paper presents the results of an experimental study of the applicability of the laser Doppler vibrometer (LDV) as a potential measurement tool for structural health monitoring in pipelines.In this case, use of the LDV has been integrated into a novel damage detection method referred to as the empirical mode decomposition (EMD) energy damage index.This method involves monitoring the free vibrations of a pipe through sensors, followed by decomposition of the sensor generated signals using EMD, and subsequently comparing an energy term of the pipe in its healthy state to that of the same pipe in a damaged state.In the experiment, a single beam LDV was utilized to acquire the vibration of a cantilever steel pipe impacted by an impulse hammer.Three cases were studied: pipes with a single half-circumferential damage, a single full-circumferential damage, and with multiple circumferential damages.The integrity of the LDV results was verified by comparison with those obtained from piezoceramic sensors bonded to the pipe surface.The results confirmed the effectiveness of the LDV and its integration into the proposed EMD damage index for identifying and locating single and multiple damages.Compared to piezoceramic sensors, the LDV, as a remote and accurate optical measurement system, provided more satisfactory identification of single and multiple damages and can therefore be successfully utilized in structural health monitoring.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".