Correction of Data Gathered by Degraded Transducers for Damage Prognosis in Composite Structures
Bibliographic record
Abstract
This paper presents an approach for the correction of data gathered for damage prognosis (DP) in composite structures. The validation setup consists of surface-bonded piezoceramic (PZT) transducers used in a Structural Health Monitoring (SHM) system with simulated bonding layer damage using Teflon masks. The modal damping around PZT mechanical resonance is used as a metric to assess and compensate for the degradation of the adhesive layer of the transducers. Modal damping is derived from electrical admittance curves using a lumped parameter model to monitor the degradation of the transducer adhesive layer. A Pitch-Catch (PC) configuration is then used to discriminate the effect of bonding degradation on actuation and sensing. It is shown that below the first mechanical resonance frequency of the PZT, degradation leads to a decrease in the amplitude of the transmitted and measured signals. Above resonance, in addition to a decrease in signal amplitude of the transmitted and measured signals, a slight linear phase delay is also observed. A Signal Correction Factor (SCF) is proposed to adjust signals based on adhesive degradation evaluated using the measured modal damping. The benefits of the SCF for prognostics feature generation are demonstrated in the frequency domain for the A0mode.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".