Structural Health Monitoring System Using Piezoelectric Networks with Tuned Lamb Waves
Bibliographic record
Abstract
The paper presents a structural health monitoring system based on propagation of tuned Lamb waves and their interference with discontinuities. The dispersion curves are studied to determine the appropriate type and dimension of transducers and to select the optimum scanning frequencies and relevant propagation modes. A piezoelectric sensor network was implemented in an aluminum plate in order to generate and to sense the wave propagation and associated reflections. The algorithm developed for damage detection relies on the comparison of undamaged and damaged responses of the structure. Combinations of filters and statistical methods were applied to detect differences in the sensor signals acquired for the two different states (damaged and undamaged), corresponding to damage reflections. In order to eliminate the false positives due to noise, a probability analysis is performed to obtain the final damage position. The software designed for the current application allows the automatic calculation of dispersion curves, it executes the scans, performs data processing, executes the detection algorithm and presents the probable damages and their positions in a graphical form. Experiments were performed with the introduction of cumulative damages in the plate such as surface and through-the-thickness holes and cuts, ranging from 7 mm to 1 mm in diameter. Additionally, a stringer was attached to the plate by a single rivet line to simulate an aircraft skin structure. Cuts originating from rivet holes and connecting adjacent rivets, as well as loosened rivets were detected by the system. The introduction of the stringer resulted in a loss of precision in the determination of the radial position of the damages near it. Also, the network revealed significant difficulties in the detection of damages beyond the stringer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".