PZT Network and Phased Array Lamb Wave Based SHM Systems
Bibliographic record
Abstract
With the application of newer materials, such as composite materials, and growing complexity and capacity of current aircraft structures, reliably and completely assess the condition of the total structures in real time is then of growing and utmost importance. PZT Network and Phased Array, Lamb wave based Structural Health Monitoring (SHM) systems were developed to be applied to thin panels. The selection of transducers, their size and selected locations for their installation are described. The development and selection of the signal generation and data acquisition systems is also presented in detail. The requirements conducing to the development and selection of these systems are laid and particularly the selection of the actuation signal applied is justified. The development of a damage detection algorithm based in the comparison of the current structural state to a reference state is described, to detect damage reflected Lamb waves. Such method was implemented in software and integrated in the SHM system developed. Subsequently the detection algorithm, based in discrete signals correlation, was further improved by incorporating statistical methods. For phased arrays, a novel damage location algorithm is presented based on the individual sensors response. A visualization method based concurrently in the statistical methods developed and superposition of the different results obtained from a test set was implemented. These tests conducted to the successful and repeatable detection of 1mm damages in a multiple damaged plate with great confidence. Finally, a brief comparison and a hybrid system implementation is presented.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".