Fault feature extraction of planetary gearboxes under nonstationary conditions based on reassigned wavelet scalogram
Bibliographic record
Abstract
Planetary gearboxes often run under time-variant conditions, thus resulting in nonstationary signals. How to extract fault features from nonstationary vibration signals is a key issue of planetary gearbox fault diagnosis. Considering the merits of reassigned wavelet scalogram, i.e. fine time-frequency resolution and free from cross term interferences, it is used to analyze the vibration signals in joint time-frequency domain. The effectiveness of reassigned wavelet scalogram in planetary gearbox fault diagnosis under nonstationary conditions is validated by both lab experimental and in-situ signals. For the lab experimental signals, the gear characteristic frequencies and their time evolving features are identified. From the comparison between normal and faulty signal analysis results, the sun gear fault is diagnosed. For the in-situ signals, their time-frequency structures are also resolved. According to the presence of periodical impulses and their repeating period, the planet gear fault is detected.
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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".