Packaging of structural health monitoring components
Bibliographic record
Abstract
Structural Health Monitoring (SHM) technologies have the potential to realize economic benefits in a broad range of commercial and defense markets. Previous research conducted by Metis Design and MIT has demonstrated the ability of Lamb waves methods to provide reliable information regarding the presence, location and type of damage in composite specimens. The present NSF funded program was aimed to study manufacturing, packaging and interface concepts for critical SHM components. The intention is to be able to cheaply manufacture robust actuating/sensing devices, and isolate them from harsh operating environments including natural, mechanical, or electrical extremes. Currently the issues related to SHM system durability have remained undressed. During the course of this research several sets of test devices were fabricated and packaged to protect the piezoelectric component assemblies for robust operation. These assemblies were then tested in hot and wet conditions, as well as in electrically noisy environments. Future work will aim to package the other supporting components such as the battery and wireless chip, as well as integrating all of these components together for operation. SHM technology will enable the reduction or complete elimination of scheduled inspections, and will allow condition-based maintenance for increased reliability and reduced overall life-cycle costs.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".