Intelligent Condition Monitoring of Wind Turbines Under Variable Damage Conditions
Bibliographic record
Abstract
Condition monitoring of wind turbines is essential to ensure their reliable and efficient operation, reducing downtime and maintenance costs while maximizing energy generation. In recent years, advancements in sensor technologies have enabled the collection of vast amounts of data from wind turbines, including ultrasonic signals. Ultrasonic guided wave (GW) signals contain valuable information about the health of the turbine components, making them a promising source for condition monitoring. This research presents a concise and innovative approach to condition monitoring of wind turbines using machine learning in conjunction with GW signals. Ultrasonic GW signals contain valuable information about turbine health, making them promising for monitoring purposes. The proposed methodology involves transforming ultrasonic data using advanced signal processing techniques and fine-tuning a pre-trained deep learning model for autonomous condition monitoring of composite wind turbine blade laminates. This integration significantly improves fault detection and health state classification accuracy compared to traditional methods, making it more practical for wind farm environments. The findings demonstrate the potential of this approach in enhancing wind turbine reliability and efficiency, contributing to a sustainable and eco-friendly energy generation process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".