A Combined Spectral Subtraction and Wavelet De-Noising Method for Bearing Fault Diagnosis
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Bibliographic record
Abstract
In this paper, the Gabor wavelet is used for wavelet filter based de-noising the vibration signal measured from faulty bearings. In this approach the parameters of the daughter wavelet corresponding to center frequency and bandwidth namely scale and shape-factor should be selected properly. The ratio of the geometric mean to the arithmetic mean of the wavelet coefficient moduli called smoothness index is used as a measure for the selection of these parameters. As bandpass filtering does not eliminate the in-band noise with frequency content on the range covered by the daughter wavelet, spectral subtraction technique is applied prior to wavelet transforming the signal. This has significantly improved the performance of the wavelet filter based de- noising method. Results are presented for both simulated and experimental data.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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