Fault Detection of Wind Turbine System Based on Deep Learning and System Identification
Bibliographic record
Abstract
Early fault diagnosis in the Wind Turbine System (WTS) can reduce the maintenance cost and increase system reliability. However, due to its complexity, analysing WTS is a challenging task. Furthermore, impacts of component faults may be negligible and difficult to detect depending on their types and severity. This paper presents an integrated framework using the Long Short-term Memory and Mixture Density Network (LSTM-MDN) with real-time system identification to diagnose incipient and subtle faults which tend to modify the measurement or dynamics of the system. At first, the LSTM-MDN is constructed to estimate the blade and pitch sub-system outputs with the corresponding variance. Then system identification based on the Auto-Regressive Integrated Moving Average with eXogenous input (ARIMAX) is implemented to generate meta-data which can reveal any changes in system dynamics. In order to reduce the false alarm rate in the presence of uncertainties, we introduce an adaptive threshold using the MDN parameters and a pruning process for detecting different operation modes. Finally, a comprehensive comparison is conducted to evaluate the effectiveness of the proposed method.
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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".