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 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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".