Compensation of piezoceramic bonding layer degradation for structural health monitoring
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Bibliographic record
Abstract
The compensation of the degradation of the bonding layer of piezoceramics used in structural health monitoring is addressed in this article. A simple admittance model is first used to measure and extract the variation of admittance parameters using the same acquisition chain which is used by the structural health monitoring system for damage monitoring. More precisely, the method uses measurable changes in physical transducer modal damping at frequencies around piezoceramic resonance to estimate the extent of degradation. Then, a finite element model is used to obtain calibration curves linking the variations in transducer modal damping to amplitude and phase of the ultrasonic signals generated or measured by the piezoceramics. Such calibration curves are obtained by simulating with the FEM the effect of varying the bonding layer coverage area and Young’s modulus on (a) admittance and (b) amplitude and phase of the ultrasonic signals. From this, a signal correction factor is developed for the dominant bonding layer coverage area degradation failure mode to compensate for the changes in amplitude and phase of guided waves generated and measured by degraded piezoceramic transducers. The measured modal damping determines the amount of bonding layer degradation from the simulated modal damping calibration curves and then the quantified bonding layer degradation amount selects the amplitude and phase correction to be applied to measured signals from the calibration curves. The benefits of the signal correction factor are demonstrated below piezoceramic resonance to improve damage imaging and localization using the Embedded Ultrasonic Structural Radar algorithm (delay and sum method) when a single transducer in a sparse array of transducers fixed to an aluminum plate is damaged due to the close proximity of drop-weight impacts. Up to a certain damage extent, the signal correction factor could allow an extension of the service life of the structural health monitoring system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 it