Normalization of gearbox vibration signal for tooth crack diagnosis under variable speed conditions
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Variable speed conditions introduce Amplitude Modulation (AM) and Frequency Modulation (FM) effects into gearbox vibration signals, which makes it difficult to distinguish between changes of tooth crack severity and speed changes. To overcome this problem, the AM and FM effects caused by speed variation need to be removed. Order tracking techniques are used to remove the FM effect. Some methods have been reported to reduce the AM effect. However, they attenuated crack information since they focused on the entire vibration signal. Besides, the performance of the reported methods on removing the AM effect was not quantitatively evaluated. In this study, a novel normalization method focusing on the Crack Induced Impulses (CII) is proposed to remove the AM effect without attenuating the tooth crack information. A modified Adaptive Chirp Mode Decomposition method is developed to obtain the CII under variable speed conditions. The peak envelope of the CII is determined using spline interpolation of its envelope peaks and is employed to remove the AM effect of the CII by normalization. Two metrics are introduced to quantitatively evaluate the performance of the proposed normalization method on removing the AM effect and preserving the tooth crack information. The effectiveness of the proposed normalization method is demonstrated using simulated gearbox signals and experimental gearbox datasets. The proposed method benefits tracking tooth crack severity progression under variable speed conditions.
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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 it