Extreme learning machines to approximate low dimensional spaces for helicopter load signal and fatigue life estimation
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
As aircraft fleets are required to expand their roles and usage, the accurate estimation of component loads in a helicopter is an important capability for safety and security reasons as well as for life cycle management and life extension efforts. Although dynamic component loads can be measured and monitored directly, these measurement methods are not reliable and are costly and difficult to maintain. Computational intelligence techniques have been successfully used for estimating helicopter dynamic loads and their resulting fatigue life using flight system and control parameters. However, other approaches work on low dimensional spaces with the advantage of smaller number of features and noise reduction due to information fusion. Nonlinear transformations have been used for this purpose, but their computation via implicit methods becomes more complex, time consuming and impractical with data growth. Moreover, the relationships between the features of the original and the target spaces are more difficult to uncover. Extreme Learning Machines (ELM) are used as an explicit functional representation for implicit methods, in particular for the t-SNE mapping. It was found that ELMs provided a good approximation to the implicit mapping, which preserves the appropriateness of the load prediction and damage estimation of critical helicopter components. In addition, the ELM model can be used for processing incoming streams of data, overcoming the limitation of the computation of the low dimensional mapping inherent to the use of implicit methods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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