Gear fault detection under time-varying rotating speed via joint application of multiscale chirplet path pursuit and multiscale morphology analysis
Bibliographic record
Abstract
This article reports a new method for gear fault detection under time-varying rotating speed. This method is based on the chirplet path pursuit and multiscale morphology analysis. The instantaneous rotating speed is extracted from the gear vibration signal using the multiscale chirplet path pursuit algorithm. According to the extracted rotation speed, the gear vibration signal is resampled at constant angle increment and as such the nonstationary signal is converted into a stationary signal. The fault-induced impulsive features can then be extracted from the resampled signal via the multiscale morphology analysis, followed by the spectrum analysis to reveal the fault characteristic frequency. Because of the low correlations between the noise and chirplet functions, the rotational speed can be extracted effectively even when the signal-to-noise ratio of the vibration signal is relatively low. In addition, the noise effect can be further suppressed by averaging the results of morphology analyses of all the scales. Therefore, the proposed approach has a good antinoise ability and is suitable for gear fault detection under time-varying rotational speed. The performance of the method has been validated by both simulation and experimental data.
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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 | 0.001 |
| 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) | 0.000 | 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 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".