Design of a PZT Sensor Network Based on Guided Lamb Waves for Structural Health Monitoring of Metallic Structures
Bibliographic record
Abstract
A Structural Health Monitoring (SHM) system of metallic structures based on guided Lamb waves is presented. Lamb waves are reflected on discontinuities in material properties and geometries such as damage. Lamb waves present advantages when applied on thin structures due to their low amplitude damping which enables them to travel longer distances. The selection of transducers, their size and selected locations in the structure are described. Additionally, the design, development and implementation of a new signal generation and data acquisition systems is presented in detail. The requirements leading to the development and selection of these systems are explained and particularly the selection of the actuation signal is discussed. A damage detection algorithm based on the comparison between the damaged structural state and a healthy reference state is used to detect damage based on reflected Lamb waves. Subsequently, the detection algorithm based on discrete signals correlation was further improved by incorporating statistical methods. Tests performed on a plate with multiple surface cuts, through the thickness cuts, loosened rivets and cuts originating from rivets resulted in repeatable detections of 1 mm damages with a probability of detection greater that 95%. New tests are currently being performed on composite panels with embedded Fiber Bragg Grating (FBG) optical sensor network to detect the fast propagating Lamb waves.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".