Federal Aviation Administration’s Probability of Detection Testing Results for Structural Health Monitoring (SHM)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The Federal Aviation Administration (FAA) has been participating with the Society of Automotive Engineers (SAE) Aerospace Industry Steering Committee (AISC) to develop a methodology for calculating the Probability of Detection (POD) for Structural Health Monitoring (SHM) for damage detection on commercial aviation. Two POD methodologies were developed: one by Dr. William Meeker, Iowa State University, and the other by Dennis Roach, Sandia National Laboratories (SNL). With Dr. Seth Kessler, Metis Design Corp, a test program of 24 samples of aluminum strips to be fatigued on MTS machines was developed. The samples were designed to meet the ASTM E647. Twelve samples had two SHM modalities on the front and back from Metis (PZT and carbon nanotubes), and the other twelve had SHM sensors from Structural Monitoring Systems (SMS) (comparative vacuum monitoring – CVM) and Acellent Technologies (PZT). The tests were performed at the FAA William J Hughes Technical Center in Atlantic City, NJ. The samples were cycled every 1500 cycles and then stopped for SHM data collection. Once the crack exceeded 0.125 inches and provided for a minimum of 15 inspections, a new sample was tested until all 12 samples were completed. The data was provided to each company to be set up in the format needed to run through the POD methodologies. Then the data was provided to Dr. Meeker and Dr. Roach for analysis. This paper will provide the results of those tests.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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) | 0.000 | 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 it