Generator Stator Current Signal Analytical Models and Signature Analysis for Fault Diagnosis of Wind Turbine Planet Bearings
Bibliographic record
Abstract
The planetary gear drivetrain-generator is essential to wind turbines, wherein planet bearings are susceptible to damage. However, it is difficult to detect planet bearing fault through traditional vibration analysis. Generator stator currents contain the drivetrain health condition information, and have low complexity. They can be collected anywhere from power wires connected to the generator stator. This enables the routine inspection on the ground to avoid the inconvenience and danger in climbing wind towers. Nevertheless, generator currents feature multiple modulations in planet bearing fault case, and pose a tremendous challenge to fault feature extraction. Therefore, thorough understanding of current characteristics is essential to fault diagnosis. In this article, the generator stator current signal analytical models under planet bearing faults are derived through magneto-electro-mechanical interaction analysis, and the amplitude modulation (AM) and frequency modulation (FM) nature is revealed consequently. Furthermore, the explicit equations of Fourier spectrum, amplitude, and frequency demodulated spectra are derived to discover analytically fault characteristics. The sidebands in Fourier spectrum, and spikes in amplitude and frequency demodulated spectra provide complementary information for more reliable fault detection. These contributions bridge the gap between the generator stator current analysis and planet bearing fault diagnosis, and provide an effective solution to wind turbine drivetrain fault diagnosis. The proposed method is validated experimentally. The outer race, inner race, and rolling element faults are detected successfully.
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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".