A State-Age-Dependent Opportunistic Intelligent Maintenance Framework for Wind Turbines Under Dynamic Wind Conditions
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
Intelligent maintenance powered by advanced sensor technology is crucial to ensure the safe and reliable operation of wind turbines. Most maintenance models are scheduled solely based on age/degradation conditions while ignoring the dynamics of wind conditions and residual lifetime that significantly affect maintenance executions. This article addresses such challenges by constructing a dynamic age-state-dependent intelligent opportunistic maintenance framework that is capable of integrating 1) degradation and age state, 2) estimation of remaining lifetime, and 3) both the positive (extra maintenance opportunities) and negative impacts (maintenance delays) of wind conditions. Specially, component-level maintenance is allowed to be postponed to balance lifetime extension and resource allocation, whose implementation interval is controlled by real-time estimations of lifetime and dynamic wind velocities. Moreover, both wind- and health-centered opportunistic maintenance are incorporated to mitigate power generation losses. The applicability and superiority of the proposed framework are validated by a case study on an Ontario wind farm.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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