Local parametric identification for <i>in situ</i> health monitoring of aircraft using distributed sensors
Bibliographic record
Abstract
The objective of this study is to provide a strategy for in situ structural health monitoring with MEMS sensors in order to reduce the high costs associated with periodic prescribed inspections of aircraft. The presented strategy focuses on the flexural waves in the medium-frequency range in order to obtain a good trade-off between damage localization and distant propagation, and to be efficient for composite materials. The method consists of performing local parametric identification of equivalent MISO systems in order to detect a parametric discontinuity when a defect is present. Simulations were performed with a hierarchical trigonometric functions set on an aluminum plate (100 cm × 75 cm × 1 mm) with a crack (5 cm × 0.007 in.). For a chirp excitation in the medium-frequency range at 1 kHz, wave propagation simulation shows the scattering around the crack in the plate. A distribution of MEMS sensors over the plate is efficient to detect a significant change in the identified local parameters, and consequently to localize the crack. However, this approach is limited to configuration with high signal-to-noise ratio. Experimental validation of the model is conducted both in frequency and time domains for healthy and cracked beams and plates. [Work supported by the Consortium for Research and Innovation in Aerospace in Quebec.]
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".