Integrated Prognosis for Wind Turbine Gearbox Condition-Based Maintenance Considering Time-Varying Load and Crack Initiation Time Uncertainty
Why this work is in the frame
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Bibliographic record
Abstract
Maintenance management in wind energy industry has great impact on the overall wind power cost. Maintenance services are either supported by wind turbine manufacturers within warranty period, or managed by wind farm owners. With condition-based maintenance (CBM) strategy, maintenance activities are scheduled based on the predicted health conditions of wind turbine components, and accurate prognostics methods are critical for effective CBM. The reported studies on integrated health prognostics considered the uncertainty in crack initiation time (CIT) uncertainty, but did not incorporate time-varying loading conditions, which could also have a significant impact on future health condition and remaining useful life (RUL) prediction. Constant loads were generally used to approximate the actual time-varying loading conditions. In this paper, an integrated prognostics method is proposed for wind turbine gearboxes considering both time-varying loading conditions and CIT uncertainty. As new condition monitoring observations are available, the distributions of both material model parameter and CIT are updated via Bayesian inference, and the failure time prediction is updated accordingly. An example is provided to demonstrate that the proposed time-varying load approach presents more benefits considering the uncertainty of CIT, with significant accuracy improvement comparing to the constant-load approach.
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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 | 0.001 | 0.003 |
| 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