Gear crack assessment based on cyclic correlation analysis
Bibliographic record
Abstract
Gear vibration signals are mainly due to the gear rotating, gear pair meshing and their coupling effects, and are usually modeled as amplitude modulation and frequency modulation (AMFM) processes. The modulation feature of vibration signals is related to the health status of gears. As well, their statistics change periodically with gear rotation, i.e. they are cyclostationary, so cyclostationary analysis is suitable to extract the modulation features of gear vibration signals, so as to detect and assess gear damage. The cyclic auto-correlation function of AMFM signals is deduced, and its properties in cyclic frequency domain are summarized. The ratio between the cyclic autocorrelation slice magnitude at cyclic frequencies of modulating frequency and 0 Hz is a monotonically increasing function of the modulation magnitude, so it is able to indicate the modulation magnitude. Since the modulation magnitude of gear vibration signals is related to localized gear damage degree, the ratio is used to assess the condition of gears. In the analysis of gear crack simulation vibration signals, it shows a monotonically increasing trend with the development of the gear damage. It has potential to early detect incipient damage, to prognosticate severe damage, and to assess gearbox damage.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.002 | 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".