Monitoring Tasks in Aerospace
Bibliographic record
Abstract
Abstract Approximately up to one-fifth of the direct operating cost of a commercial civilian fixed-wing aircraft is projected to be due to inspection and maintenance alone. Managing aircraft health with minimal human intervention and technologies that can perform continuous or on-demand monitoring/evaluation of aircraft components without having to take the aircraft out of service can have a significant impact on increasing availability while reducing maintenance cost. The ambition of these monitoring technologies is to shift aircraft maintenance practice from planned maintenance (PM), where the aircraft is taken out of service for scheduled inspection/maintenance, to condition-based maintenance (CBM), where aircraft is taken out of service only when maintenance is required, while maintaining the required levels of safety. Structural health monitoring (SHM) techniques can play a vital role in progressing towards CBM practice. Therefore, this chapter aims to provide the reader with a brief overview of the different SHM techniques and their use, as well as, challenges in implementing them for aircraft applications.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".