An approach to fatigue damage estimation of helicopter rotating components using computational intelligence techniques
Bibliographic record
Abstract
In this paper we present a computational intelligence approach to estimate fatigue usage in rotating components based on real aircraft data (Australian Black Hawk S-70A-9 flight load survey data). The load time signal for the main rotor pushrod in forward level flight is first predicted using only input data from the flight state and control system parameters through a computational intelligence model. The subsequent fatigue usage is then estimated using adaptations of standard techniques, such as the Rainflow cycle counting method. More accurate fatigue accumulation and remaining life predictions can possibly be made considering the real operational flight load spectra and not just based on design mission estimations, accounting for the change in use of platforms during their in-service lives. This approach is particularly devoted to rotating components and avoids the use of additional sensors, specifically challenging when dynamic components are considered. Copyright© (2013) by the American Helicopter Society International.
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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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".