Load Monitoring of Aerospace Structures Using Micro-Electro-Mechanical Systems (MEMS)
Bibliographic record
Abstract
The National Research Council of Canada has developed Structural Health Monitoring (SHM) test platforms for load and damage monitoring, sensor system testing and validation. One of the SHM platform consists of two 2.25 meter long, simple cantilever aluminium beams that provide a perfect scenario for evaluating the capability of a load monitoring system to measure bending, torsion and shear loads. In addition to static and quasi-static loading procedures, these structures can be fatigue loaded using a realistic aircraft usage spectrum while SHM and load monitoring systems are assessed for their performance and accuracy. In this study, Micro-Electro-Mechanical Systems (MEMS), consisting of triads of gyroscopes, accelerometers and magnetometers, were used to compute changes in angles at discrete stations along the structure. A Least Squares based algorithm was developed for polynomial fitting of the different data obtained from the MEMS installed in several spatial locations of the structure. The angles obtained from the MEMS sensors were fitted with a second, third and/or fourth order degree polynomial surface, enabling the calculation of displacements at every point. The use of a novel Kalman filter architecture was evaluated for an accurate angle and subsequent displacement estimation. The outputs of the newly developed algorithms were then compared to the displacements obtained from the Linear Variable Displacement Transducers (LVDT) connected to the structures. The determination of the best Least Squares based polynomial fit order enabled the application of derivative operators with enough accuracy to permit the calculation of strains along the structure. The calculated strain values were subsequently compared to the measurements obtained from reference strain gauges installed at different locations on the structure. This new approach for load monitoring was able to provide accurate estimates of applied strains and loads.
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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 | 0.001 | 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".