In situ characterization technique to increase robustness of imaging approaches in structural health monitoring using guided waves
Bibliographic record
Abstract
The performance of guided wave imaging strategies used in Structural Health Monitoring relies on the accurate knowledge of mechanical properties for proper damage detection and localization. In order to increase the performance and robustness of such algorithms, it is desirable to implement autonomous approaches that can characterize the mechanical properties of the structure whatsoever the environmental and operational conditions. This article presents an innovative in situ and integrated characterization procedure based on guided waves that evaluates the thermo-mechanical properties of a structure when subjected to thermal variations prior to imaging using the same set of piezoceramic transducers used for imaging. These properties are then exploited in the damage imaging using a correlation-based algorithm (Excitelet) combined with the optimal baseline subtraction. The characterization strategy uses a genetic algorithm to identify the optimal set of mechanical properties leading to the best correlation between an analytical formulation of dispersed guided waves propagation and experimental measurements. The strategy is assessed experimentally on an aluminum plate with three sparse bonded piezoceramic transducers used for both characterization and imaging at various temperatures, representative of operational conditions of an aircraft. An artificial damage is subsequently introduced in the plate, and the effect of the accuracy of the mechanical properties estimation on imaging is assessed through the detection capability, positioning, accuracy, and correlation amplitude. The approach is then compared to three imaging methods, namely, baseline-free imaging, imaging without considering thermo-mechanical effects, and imaging using stretching methods traditionally used to compensate for temperature effects.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".